Last updated on 2025-12-04 07:50:56 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 4.10.1 | 74.07 | 643.78 | 717.85 | ERROR | --no-vignettes |
| r-devel-linux-x86_64-debian-gcc | 4.10.1 | 59.81 | 417.37 | 477.18 | ERROR | --no-vignettes |
| r-devel-linux-x86_64-fedora-clang | 4.10.1 | 142.00 | 1044.77 | 1186.77 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 4.10.1 | 210.00 | 1004.22 | 1214.22 | ERROR | |
| r-devel-windows-x86_64 | 4.10.1 | 109.00 | 305.00 | 414.00 | OK | --no-examples --no-tests --no-vignettes |
| r-patched-linux-x86_64 | 4.10.1 | 96.28 | 588.78 | 685.06 | ERROR | --no-vignettes |
| r-release-linux-x86_64 | 4.10.1 | 81.47 | 598.18 | 679.65 | ERROR | --no-vignettes |
| r-release-macos-arm64 | 4.10.1 | OK | ||||
| r-release-macos-x86_64 | 4.10.1 | 47.00 | 488.00 | 535.00 | OK | |
| r-release-windows-x86_64 | 4.10.1 | 116.00 | 231.00 | 347.00 | OK | --no-examples --no-tests --no-vignettes |
| r-oldrel-macos-arm64 | 4.10.1 | NOTE | ||||
| r-oldrel-macos-x86_64 | 4.10.1 | 66.00 | 475.00 | 541.00 | NOTE | |
| r-oldrel-windows-x86_64 | 4.10.1 | 140.00 | 292.00 | 432.00 | OK | --no-examples --no-tests --no-vignettes |
Version: 4.10.1
Flags: --no-vignettes
Check: tests
Result: ERROR
Running ‘requireNamespaceTest.R’ [3s/4s]
Running ‘testthat.R’ [356s/194s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # File tests/testthat.R in package ergm, part of the Statnet suite of packages
> # for network analysis, https://statnet.org .
> #
> # This software is distributed under the GPL-3 license. It is free, open
> # source, and has the attribution requirements (GPL Section 7) at
> # https://statnet.org/attribution .
> #
> # Copyright 2003-2025 Statnet Commons
> ################################################################################
> library(testthat)
> library(ergm)
Loading required package: network
'network' 1.19.0 (2024-12-08), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4.10.1 (2025-08-26), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.
>
> test_check("ergm")
Starting 2 test processes.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1 2
> test-basis.R: 3 4 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1 2
> test-basis.R: 3
> test-basis.R: 4 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8 9
> test-basis.R: 10 11 12
> test-basis.R: 13
> test-basis.R: 14 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12 13
> test-basis.R: 14 15 16 .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1 The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1 The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1 The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1 The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: Attaching package: 'statnet.common'
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: The following objects are masked from 'package:base':
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: attr, order, replace
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: Unable to match target stats. Using MCMLE estimation.
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-bridge-target.stats.R: Iteration 1 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 1
> test-basis.R: The log-likelihood improved by 0.0072.
> test-bridge-target.stats.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: The log-likelihood improved by 0.0219.
> test-bridge-target.stats.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-bridge-target.stats.R: Finished MCMLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Using 16 bridges: 1
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-basis.R: 2 3 4 5
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-basis.R: 6
> test-basis.R: 7
> test-bridge-target.stats.R: 2
> test-basis.R: 8
> test-bridge-target.stats.R: 3
> test-basis.R: 9
> test-basis.R: 10
> test-bridge-target.stats.R: 4
> test-basis.R: 11
> test-bridge-target.stats.R: 5
> test-basis.R: 12
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-basis.R: 13
> test-basis.R: 14
> test-bridge-target.stats.R: 8
> test-basis.R: 15
> test-bridge-target.stats.R: 9
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 10 11 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 15
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1 The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1 The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1 The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1 The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-bridge-target.stats.R: 16 .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: This model was fit using MCMC. To examine model diagnostics and check
> test-bridge-target.stats.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1 The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 6
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 9
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-bridge-target.stats.R: 10
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges:
> test-basis.R: 1
> test-basis.R: 2
> test-bridge-target.stats.R: 11
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8 9 10 11
> test-bridge-target.stats.R: 12
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-bridge-target.stats.R: 13
> test-basis.R: 16 .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 14
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1 The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-bridge-target.stats.R: 15
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1 The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-bridge-target.stats.R: 16
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1 The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1 The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 8
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Setting up bridge sampling...
> test-bridge-target.stats.R: 9
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1 2 3
> test-basis.R: 4
> test-basis.R: 5
> test-bridge-target.stats.R: 10
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8 9
> test-basis.R: 10 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15 16
> test-bridge-target.stats.R: 11
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Iteration 1 of at most 60:
> test-checkpointing.R: Saving state in '/home/hornik/tmp/scratch/Rtmpgj1rKW/file1a286a667847dd_001.RData'.
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-checkpointing.R: The log-likelihood improved by 0.0213.
> test-checkpointing.R: Step length converged once. Increasing MCMC sample size.
> test-checkpointing.R: Iteration 2 of at most 60:
> test-checkpointing.R: Saving state in '/home/hornik/tmp/scratch/Rtmpgj1rKW/file1a286a667847dd_002.RData'.
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 9
> test-checkpointing.R: The log-likelihood improved by 0.0238.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-checkpointing.R: Using 16 bridges: 1
> test-checkpointing.R: 2
> test-checkpointing.R: 3
> test-checkpointing.R: 4
> test-checkpointing.R: 5
> test-checkpointing.R: 6
> test-checkpointing.R: 7 8
> test-checkpointing.R: 9 10
> test-bridge-target.stats.R: 10
> test-checkpointing.R: 11
> test-checkpointing.R: 12
> test-checkpointing.R: 13 14
> test-bridge-target.stats.R: 11
> test-checkpointing.R: 15 16
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 12
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Resuming from state saved in '/home/hornik/tmp/scratch/Rtmpgj1rKW/file1a286a667847dd_002.RData'.
> test-checkpointing.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-checkpointing.R: 1
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-checkpointing.R: The log-likelihood improved by 0.0145.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-checkpointing.R: Using 16 bridges: 1
> test-checkpointing.R: 2 3
> test-bridge-target.stats.R: 2
> test-checkpointing.R: 4
> test-checkpointing.R: 5
> test-checkpointing.R: 6
> test-checkpointing.R: 7
> test-checkpointing.R: 8
> test-checkpointing.R: 9
> test-bridge-target.stats.R: 3
> test-checkpointing.R: 10
> test-checkpointing.R: 11
> test-checkpointing.R: 12
> test-checkpointing.R: 13
> test-checkpointing.R: 14 15
> test-checkpointing.R: 16
> test-bridge-target.stats.R: 4
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constrain-degrees-edges.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constrain-degrees-edges.R: Iteration 1 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:5.2e-06
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.05768.
> test-constrain-degrees-edges.R: Iteration 2 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:3.7e-03
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.09813.
> test-constrain-degrees-edges.R: Finished CD.
> test-constrain-degrees-edges.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-degrees-edges.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R:
> test-constrain-blockdiag.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-constrain-blockdiag.R: * 'news(package="ergm.count")' for changes since last version
> test-constrain-blockdiag.R: * 'citation("ergm.count")' for citation information
> test-constrain-blockdiag.R: * 'https://statnet.org' for help, support, and other information
> test-constrain-blockdiag.R:
> test-constrain-degrees-edges.R: Best valid proposal 'CondInDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constrain-dind.R: Iteration 1 of at most 60:
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: 1 Optimizing with step length 1.0000.
> test-constrain-dind.R: The log-likelihood improved by 0.0020.
> test-constrain-dind.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constrain-dind.R: Finished MCMLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-constrain-dind.R: Bridging between the dyad-independent submodel and the full model...
> test-constrain-dind.R: Setting up bridge sampling...
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Using 16 bridges:
> test-constrain-dind.R: 1
> test-constrain-dind.R: 2
> test-constrain-dind.R: 3 4
> test-constrain-dind.R: 5
> test-constrain-dind.R: 6
> test-constrain-dind.R: 7
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: 8
> test-constrain-dind.R: 9
> test-constrain-dind.R: 10
> test-constrain-dind.R: 11 12
> test-constrain-dind.R: 13
> test-constrain-dind.R: 14 15 16
> test-constrain-dind.R: .
> test-constrain-dind.R: Bridging finished.
> test-constrain-dind.R:
> test-constrain-dind.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-dind.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by < 0.0001.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Fitting the dyad-independent submodel...
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Using 16 bridges: 1 2
> test-drop.R: 3
> test-drop.R: 4
> test-drop.R: 5 6
> test-drop.R: 7
> test-drop.R: 8 9
> test-drop.R: 10 11
> test-drop.R: 12
> test-drop.R: 13
> test-drop.R: 14 15
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-drop.R: 16 .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-drop.R: 1
> test-drop.R: Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by 0.0018.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-drop.R: Using 16 bridges: 1 2 3
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: 4
> test-drop.R: 5
> test-drop.R: 6 7
> test-drop.R: 8
> test-drop.R: 9 10 11
> test-drop.R: 12
> test-drop.R: 13
> test-drop.R: 14
> test-drop.R: 15 16 .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: Evaluating network in model.
> test-drop.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-drop.R: 'ergm:MH_SPDyad'.
> test-drop.R: Initializing model...
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R: Model initialized.
> test-drop.R: Using initial method 'MPLE'.
> test-drop.R: Initial parameters provided by caller: None.
> test-drop.R: number of free parameters: 7
> test-drop.R: number of fixed parameters: 0
> test-drop.R: Observed statistic(s) triangle and kstar5 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Fitting initial model.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Density guard set to 10000 from an initial count of 3 edges.
> test-drop.R:
> test-drop.R: Iteration 1 of at most 3 with parameter:
> test-drop.R: edges triangle degree2 kstar2 kstar5
> test-drop.R: 1.313175 -Inf 6.009173 -4.163221 -Inf
> test-drop.R: gwdegree gwdegree.decay
> test-drop.R: -2.860022 1.666671
> test-drop.R: Starting unconstrained MCMC...
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Back from unconstrained MCMC.
> test-drop.R: New interval = 512.
> test-drop.R: Average estimating function values:
> test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay
> test-drop.R: -0.29629630 -0.11934156 -0.11934156 -0.57005199 0.06446661
> test-drop.R: Starting MCMLE Optimization...
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: Using lognormal metric (see control.ergm function).
> test-drop.R: Optimizing loglikelihood
> test-drop.R: Starting MCMC s.e. computation.
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-drop.R: The log-likelihood improved by 0.0385.
> test-constraints.R: All terms are either offsets or extreme values. No optimization is performed.
> test-constraints.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges: 1
> test-drop.R: Estimated covariance matrix of the statistics has nullity 3. Effective parameter number adjusted to 2.
> test-drop.R: Test statistic: T^2 = 21.94136, with 2 free parameter(s) and 240.9959 degrees of freedom.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-drop.R: Fitting the dyad-independent submodel...
> test-constraints.R: 2
> test-drop.R: Dyad-independent submodel MLE has likelihood -11.02185 at:
> test-drop.R: [1] -2.639057 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
> test-drop.R: [8] 0.000000
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-constraints.R: 3
> test-drop.R: Initializing model and proposals...
> test-constraints.R: 4
> test-drop.R: Model and proposals initialized.
> test-drop.R: Using 16 bridges: Running theta=[-1.175967, -Inf, 6.177257,-4.435272, -Inf,-1.386658, 1.950625, 0.000000].
> test-drop.R: Running theta=[-1.270360, -Inf, 5.778724,-4.149126, -Inf,-1.297196, 1.824778, 0.000000].
> test-constraints.R: 5
> test-drop.R: Running theta=[-1.364753, -Inf, 5.380191,-3.862979, -Inf,-1.207735, 1.698931, 0.000000].
> test-drop.R: Running theta=[-1.459146, -Inf, 4.981659,-3.576833, -Inf,-1.118273, 1.573085, 0.000000].
> test-drop.R: Running theta=[-1.553539, -Inf, 4.583126,-3.290686, -Inf,-1.028811, 1.447238, 0.000000].
> test-drop.R: Running theta=[-1.6479316, -Inf, 4.1845933,-3.0045393, -Inf,-0.9393491, 1.3213911, 0.0000000].
> test-drop.R: Running theta=[-1.7423245, -Inf, 3.7860606,-2.7183927, -Inf,-0.8498873, 1.1955444, 0.0000000].
> test-constraints.R: 6
> test-drop.R: Running theta=[-1.8367174, -Inf, 3.3875279,-2.4322461, -Inf,-0.7604255, 1.0696976, 0.0000000].
> test-drop.R: Running theta=[-1.9311104, -Inf, 2.9889952,-2.1460995, -Inf,-0.6709636, 0.9438508, 0.0000000].
> test-drop.R: Running theta=[-2.0255033, -Inf, 2.5904625,-1.8599529, -Inf,-0.5815018, 0.8180040, 0.0000000].
> test-drop.R: Running theta=[-2.1198962, -Inf, 2.1919298,-1.5738063, -Inf,-0.4920400, 0.6921573, 0.0000000].
> test-constraints.R: 7
> test-drop.R: Running theta=[-2.2142891, -Inf, 1.7933971,-1.2876597, -Inf,-0.4025782, 0.5663105, 0.0000000].
> test-drop.R: Running theta=[-2.3086821, -Inf, 1.3948644,-1.0015131, -Inf,-0.3131164, 0.4404637, 0.0000000].
> test-drop.R: Running theta=[-2.4030750, -Inf, 0.9963317,-0.7153665, -Inf,-0.2236545, 0.3146169, 0.0000000].
> test-drop.R: Running theta=[-2.4974679, -Inf, 0.5977990,-0.4292199, -Inf,-0.1341927, 0.1887702, 0.0000000].
> test-drop.R: Running theta=[-2.59186086, -Inf, 0.19926635,-0.14307330, -Inf,-0.04473091, 0.06292339, 0.00000000].
> test-constraints.R: 8
> test-drop.R: .
> test-drop.R: Bridge sampling finished. Collating...
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constraints.R: 9
> test-constraints.R: 10
> test-constraints.R: 11
> test-constraints.R: 12
> test-constraints.R: 13
> test-constraints.R: 14
> test-constraints.R: 15
> test-constraints.R: 16
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'c
> test-constraints.R: .
> test-ergm-proposal-unload.R: itation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constraints.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constraints.R: Iteration 1 of at most 60:
> test-constraints.R: Convergence test P-value:1.1e-05
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.07919.
> test-constraints.R: Iteration 2 of at most 60:
> test-constraints.R: Convergence test P-value:3.7e-02
> test-constraints.R: 1 The log-likelihood improved by 0.01687.
> test-constraints.R: Iteration 3 of at most 60:
> test-constraints.R: Convergence test P-value:7e-01
> test-constraints.R: Convergence detected. Stopping.
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.0006029.
> test-constraints.R: Finished CD.
> test-constraints.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constraints.R: Iteration 1 of at most 60:
> test-constraints.R: 1
> test-constraints.R: Optimizing with step length 1.0000.
> test-constraints.R: The log-likelihood improved by 0.0263.
> test-constraints.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constraints.R: Finished MCMLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Setting up bridge sampling...
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges: 1
> test-constraints.R: 2
> test-constraints.R: 3
> test-constraints.R: 4
> test-constraints.R: 5
> test-constraints.R: 6
> test-constraints.R: 7
> test-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constraints.R: 8
> test-constraints.R: 9
> test-constraints.R: 10
> test-constraints.R: 11 12
> test-constraints.R: 13
> test-constraints.R: 14 15
> test-constraints.R: 16
> test-constraints.R: .
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-constraints.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constraints.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (binary, valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ctriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ctriad (binary)
> test-ergm-term-doc.R: Cyclic triples
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: localtriangle(x) (binary)
> test-ergm-term-doc.R: Triangles within neighborhoods
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1) (binary)
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: opentriad (binary)
> test-ergm-term-doc.R: Open triads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: threetrail(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: threepath(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Three-trails
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: triangle(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: triangles(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangles
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: tripercent(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangle percentage
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ttriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ttriad (binary)
> test-ergm-term-doc.R: Transitive triples
> test-ergm-term-doc.R: Found 31 matching ergm terms:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Definitions for term(s) b2factor :
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the
> test-ergm-term-doc.R: unique values of the attr attribute. Each of these statistics
> test-ergm-term-doc.R: gives the number of times a node with that attribute in the second mode of
> test-ergm-term-doc.R: the network appears in an edge. The second mode of a bipartite network
> test-ergm-term-doc.R: object is sometimes known as the "event" mode.
> test-ergm-term-doc.R: Keywords: bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3factor' were found. Try searching with search='b3factor'instead.
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm-term-doc.R: Found 48 matching ergm terms:
> test-ergm-term-doc.R: B(formula, form) (valued)
> test-ergm-term-doc.R: Wrap binary terms for use in valued models
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Impose a curved structure on term parameters
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Exp(formula) (valued)
> test-ergm-term-doc.R: Exponentiate a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: For(...) (valued)
> test-ergm-term-doc.R: A for operator for terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Label(formula, label, pos) (valued)
> test-ergm-term-doc.R: Modify terms' coefficient names
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Log(formula, log0=-1/sqrt(.Machine$double.eps)) (valued)
> test-ergm-term-doc.R: Take a natural logarithm of a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Prod(formulas, label) (valued)
> test-ergm-term-doc.R: A product (or an arbitrary power combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: S(formula, attrs) (valued)
> test-ergm-term-doc.R: Evaluation on an induced subgraph
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Sum(formulas, label) (valued)
> test-ergm-term-doc.R: A sum (or an arbitrary linear combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiff(attr, pow=1, form="sum") (valued)
> test-ergm-term-doc.R: Absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiffcat(attr, base=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Categorical absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atleast(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values greater than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atmost(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values less than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: attrcov(attr, mat, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate by attribute pairing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalties(threshold=0) (valued)
> test-ergm-term-doc.R: Cyclical ties
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Cyclical weights
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edgecov(x, attrname=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edges (valued)
> test-ergm-term-doc.R: nonzero (valued)
> test-ergm-term-doc.R: Number of edges in the network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: equalto(value=0, tolerance=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values equal to a specific value (within tolerance)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: greaterthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly greater than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ininterval(lower=-Inf, upper=+Inf, open=c(TRUE,TRUE)) (valued)
> test-ergm-term-doc.R: Number of dyads whose values are in an interval
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mm(attrs, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Mixing matrix cells and margins
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mutual(form="min",threshold=0) (valued)
> test-ergm-term-doc.R: Mutuality
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecov(attr, form="sum") (valued)
> test-ergm-term-doc.R: nodemain(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of undirected dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodefactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of in-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeifactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: match(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Uniform homophily and differential homophily
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of out-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeofactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: receiver(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Receiver effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sender(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Sender effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: smallerthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly smaller than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Undirected degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sum(pow=1) (valued)
> test-ergm-term-doc.R: Sum of dyad values (optionally taken to a power)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: transitiveweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Transitive weights
> test-ergm-term-doc.R: Found 4 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DiscUnif(a,b)
> test-ergm-term-doc.R: Discrete Uniform reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: StdNormal
> test-ergm-term-doc.R: Standard Normal reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Unif(a,b)
> test-ergm-term-doc.R: Continuous Uniform reference
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 1 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R: Definitions for term(s) Bernoulli :
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference: Specifies each
> test-ergm-term-doc.R: dyad's baseline distribution to be Bernoulli with probability of
> test-ergm-term-doc.R: the tie being 0.5 . This is the only reference measure used
> test-ergm-term-doc.R: in binary mode.
> test-ergm-term-doc.R: Keywords: binary, discrete, finite, nonnegative
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'Cernoulli' were found. Try searching with search='Cernoulli'instead.
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrees
> test-ergm-term-doc.R: Preserve the receiver degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 16 matching ergm terms:
> test-ergm-term-doc.R: Dyads(fix=NULL, vary=NULL)
> test-ergm-term-doc.R: Constrain fixed or varying dyad-independent terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blockdiag(attr)
> test-ergm-term-doc.R: Block-diagonal structure constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blocks(attr=NULL, levels=NULL, levels2=FALSE, b1levels=NULL, b2levels=NULL)
> test-ergm-term-doc.R: Constrain blocks of dyads defined by mixing type on a vertex attribute.
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise(p01, p10)
> test-ergm-term-doc.R: A soft constraint to adjust the sampled distribution for
> test-ergm-term-doc.R: dyad-level noise with known perturbation probabilities
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: egocentric(attr=NULL, direction="both")
> test-ergm-term-doc.R: Preserve values of dyads incident on vertices with given attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixallbut(free.dyads)
> test-ergm-term-doc.R: Preserve the dyad status in all but the given edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixedas(fixed.dyads, present, absent)
> test-ergm-term-doc.R: Fix specific dyads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: hamming
> test-ergm-term-doc.R: Preserve the hamming distance to the given network (BROKEN: Do NOT Use)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: observed
> test-ergm-term-doc.R: Preserve the observed dyads of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Definitions for term(s) b1degrees :
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks: For bipartite networks, preserve the degree for the first mode of each vertex of the given
> test-ergm-term-doc.R: network, while allowing the degree for the second mode to vary.
> test-ergm-term-doc.R: Keywords: bipartite
> test-ergm-term-doc.R:
> test-ergm.bridge.llr.R: 2
> test-ergm-term-doc.R: No terms named 'b3degrees' were found. Try searching with search='b3degrees'instead.
> test-ergm-term-doc.R: Found 2 matching ergm proposals:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 5 matching ergm proposals:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DistRLE
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Found 0 matching ergm proposals:
> test-ergm-term-doc.R: Found 18 matching ergm proposals:
> test-ergm-term-doc.R: BDStratTNT
> test-ergm-term-doc.R: TNT proposal with degree bounds, stratification, and a blocks constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegree
> test-ergm-term-doc.R: MHp for degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeDist
> test-ergm-term-doc.R: MHp for degreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeMix
> test-ergm-term-doc.R: MHp for degree mix constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegree
> test-ergm-term-doc.R: MHp for idegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegreeDist
> test-ergm-term-doc.R: MHp for idegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegree
> test-ergm-term-doc.R: MHp for odegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegreeDist
> test-ergm-term-doc.R: MHp for odegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingConstantEdges
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoiseTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Definitions for proposal(s) randomtoggle :
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Reference: Bernoulli Class: cross-sectional
> test-ergm-term-doc.R: May Enforce: .dyads bd
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No proposals named 'mandomtoggle' were found. Try searching with search='mandomtoggle'instead.
> test-ergm.bridge.llr.R: 3
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-term-doc.R: * 'c
> test-ergm-term-doc.R: itation("ergm.count")' for citation information
> test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-term-doc.R:
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergmMPLE.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-ergmMPLE.R: Obtaining the responsible dyads.
> test-ergmMPLE.R: Evaluating the predictor and response matrix.
> test-ergmMPLE.R: Maximizing the pseudolikelihood.
> test-ergmMPLE.R: Finished MPLE.
> test-ergmMPLE.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R:
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-gflomiss.R: 1 Optimizing with step length 1.0000.
> test-gflomiss.R: The log-likelihood improved by 0.0002.
> test-ergm.bridge.llr.R: 11
> test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: Using 16 bridges:
> test-gflomiss.R: 1
> test-gflomiss.R: 2
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: 3
> test-gflomiss.R: 4
> test-gflomiss.R: 5
> test-ergm.bridge.llr.R: 15
> test-gflomiss.R: 6
> test-gflomiss.R: 7
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: 8 9
> test-gflomiss.R: 10
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-gflomiss.R: 11
> test-gflomiss.R: 12
> test-gflomiss.R: 13
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gflomiss.R: 14 15
> test-gflomiss.R: 16
> test-gflomiss.R: .
> test-ergm.bridge.llr.R: 2
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 3
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-gflomiss.R: 1
> test-ergm.bridge.llr.R: 9
> test-gflomiss.R: Optimizing with step length 1.0000.
> test-gflomiss.R: The log-likelihood improved by 0.0241.
> test-ergm.bridge.llr.R: 10
> test-gflomiss.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: Using 16 bridges: 1 2
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: 3
> test-gflomiss.R: 4
> test-ergm.bridge.llr.R: 15
> test-gflomiss.R: 5 6
> test-gflomiss.R: 7 8
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: 9
> test-gflomiss.R: 10
> test-gflomiss.R: 11
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-gflomiss.R: 12
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gflomiss.R: 13
> test-gflomiss.R: 14
> test-gflomiss.R: 15 16 .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gmonkmiss.R: odegree3
> test-gmonkmiss.R: odegree4 odegree5 odegree6
> test-gmonkmiss.R: 1 5 7 5
> test-gmonkmiss.R: idegree2 idegree3 idegree4 idegree5 idegree6 idegree7 idegree8 idegree10
> test-gmonkmiss.R: 3 5 1 3 2 1 1 1
> test-gmonkmiss.R: idegree11
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 2
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-ergm.bridge.llr.R: 3
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.6245.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-ergm.bridge.llr.R: 10
> test-gmonkmiss.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-gmonkmiss.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-gmonkmiss.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network
> test-gmonkmiss.R: satisfies all constraints.
> test-gmonkmiss.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-gmonkmiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 11
> test-gmonkmiss.R: Convergence test P-value:3.3e-34
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.4205.
> test-gmonkmiss.R: Iteration 2 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.8e-14
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.1501.
> test-gmonkmiss.R: Iteration 3 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2e-04
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.03536.
> test-gmonkmiss.R: Iteration 4 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.6e-01
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.007343.
> test-gmonkmiss.R: Iteration 5 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2.4e-01
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.00569.
> test-gmonkmiss.R: Iteration 6 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:9.9e-02
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.00904.
> test-gmonkmiss.R: Iteration 7 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:7.6e-01
> test-gmonkmiss.R: Convergence detected. Stopping.
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.001102.
> test-gmonkmiss.R: Finished CD.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.4514.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 4
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0105.
> test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 5
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-ergm.bridge.llr.R: 6
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 10
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.7035.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 13
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-ergm.bridge.llr.R: 14
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-metrics.R: 1 Optimizing with step length 0.4613.
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-metrics.R: The log-likelihood improved by 4.1429.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.8364.
> test-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-metrics.R: The log-likelihood improved by 4.7215.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-miss-dep.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss-dep.R: Convergence test P-value:3.6e-48
> test-miss-dep.R: 1
> test-metrics.R: The log-likelihood improved by 1.1346.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: The log-likelihood improved by 1.676.
> test-miss-dep.R: Iteration 2 of at most 60:
> test-miss-dep.R: Convergence test P-value:1e-13
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1129.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss-dep.R: 1 The log-likelihood improved by 0.2714.
> test-miss-dep.R: Iteration 3 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.3e-09
> test-miss-dep.R: 1 The log-likelihood improved by 0.1603.
> test-miss-dep.R: Iteration 4 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0037.
> test-miss-dep.R: Convergence test P-value:1.3e-02
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.025.
> test-miss-dep.R: Iteration 5 of at most 60:
> test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss-dep.R: Convergence test P-value:5e-02
> test-miss-dep.R: 1 The log-likelihood improved by 0.01534.
> test-miss-dep.R: Iteration 6 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: Convergence test P-value:7.1e-01
> test-miss-dep.R: Convergence detected. Stopping.
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.0005305.
> test-miss-dep.R: Finished CD.
> test-miss-dep.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4018.
> test-metrics.R: The log-likelihood improved by 3.2780.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.6020.
> test-metrics.R: The log-likelihood improved by 3.4584.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: Post-burnin sample is constant; returning.
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.2132.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: 1 Optimizing with step length 1.0000.
> test-miss-dep.R: The log-likelihood improved by 0.0015.
> test-miss-dep.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss-dep.R: Finished MCMLE.
> test-miss-dep.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss-dep.R: Using 16 bridges: 1
> test-metrics.R: The log-likelihood improved by 0.0377.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss-dep.R: 2
> test-miss-dep.R: 3
> test-miss-dep.R: 4
> test-miss-dep.R: 5
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: 6
> test-miss-dep.R: 7
> test-miss-dep.R: 8
> test-miss-dep.R: 9
> test-miss-dep.R: 10
> test-miss-dep.R: 11
> test-miss-dep.R: 12
> test-miss-dep.R: 13
> test-metrics.R: 1 Optimizing with step length 0.4158.
> test-miss-dep.R: 14
> test-metrics.R: The log-likelihood improved by 1.9115.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss-dep.R: 15
> test-miss-dep.R: 16 .
> test-miss-dep.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-miss-dep.R: model likelihood is only implemented for dyad-independent constraints
> test-miss-dep.R: at this time. Number of observations is similarly poorly defined. This
> test-miss-dep.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-miss-dep.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-miss-dep.R: distribution and constraints.
> test-miss-dep.R:
> test-miss-dep.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss-dep.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: n=
> test-miss.CD.R: 20, density=0.1, missing=0.1
> test-metrics.R: 1 Optimizing with step length 0.4804.
> test-metrics.R: The log-likelihood improved by 2.5519.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.9415.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:3e-13
> test-miss.CD.R: 1 The log-likelihood improved by 0.2096.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-12
> test-miss.CD.R: 1 The log-likelihood improved by 0.1974.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:5.8e-11
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.341.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4e-15
> test-miss.CD.R: 1 The log-likelihood improved by 0.1744.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1226.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.3e-16
> test-miss.CD.R: 1 The log-likelihood improved by 0.6862.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.3e-15
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.3025.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-19
> test-miss.CD.R: 1 The log-likelihood improved by 0.1888.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.3e-17
> test-miss.CD.R: 1 The log-likelihood improved by 0.2862.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.6e-07
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.2007.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.1e-02
> test-miss.CD.R: 1 The log-likelihood improved by 0.03854.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-05
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1259.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.0004877.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.5e-68
> test-miss.CD.R: 1 The log-likelihood improved by 0.7099.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.6e-64
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.5925.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 0.3934.
> test-miss.CD.R: Convergence test P-value:9.6e-53
> test-miss.CD.R: 1 The log-likelihood improved by 0.6327.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-metrics.R: The log-likelihood improved by 4.1457.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.1e-37
> test-miss.CD.R: 1 The log-likelihood improved by 0.8025.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-30
> test-miss.CD.R: 1 The log-likelihood improved by 0.583.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-19
> test-miss.CD.R: 1 The log-likelihood improved by 0.6591.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.2e-04
> test-miss.CD.R: 1 The log-likelihood improved by 0.05663.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.4e-01
> test-miss.CD.R: 1 The log-likelihood improved by 0.007268.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.1e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.8651.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-54
> test-miss.CD.R: 1 The log-likelihood improved by 0.5854.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.4e-52
> test-miss.CD.R: 1 The log-likelihood improved by 0.6164.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.5e-46
> test-miss.CD.R: 1 The log-likelihood improved by 0.6486.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-32
> test-miss.CD.R: 1
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.3360.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 1.361.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.8e-14
> test-miss.CD.R: 1 The log-likelihood improved by 0.379.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-03
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.05118.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.9e-02
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.03478.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.5e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.001186.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0827.
> test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-miss.CD.R: Network statistics:
> test-miss.CD.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.CD.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.CD.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.CD.R: 0 0 0 0 0 0 0
> test-miss.CD.R: Correct estimate = -2.028148
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-283
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.712.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-235
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.493.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.3e-184
> test-metrics.R: 1
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 0.3701.
> test-metrics.R: The log-likelihood improved by 2.3554.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 1.167.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.9e-150
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2
> test-metrics.R: 1
> test-metrics.R: 2
> test-metrics.R: 3 4 5 6 7 8
> test-metrics.R: 9
> test-metrics.R: 10
> test-metrics.R: 11 12 Optimizing with step length 0.5218.
> test-metrics.R: The log-likelihood improved by 2.9696.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-147
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-metrics.R: 1 2
> test-metrics.R: 3 Optimizing with step length 0.8048.
> test-metrics.R: The log-likelihood improved by 1.8226.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.2e-155
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.8e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.2705.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-153
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:8e-150
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss.CD.R: 1 2
> test-metrics.R: The log-likelihood improved by 0.0012.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-metrics.R: Convergence test p-value: 0.0099. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:6.7e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 13 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 14 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-162
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 15 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: 1 2 3 4 5 6 7 8 9
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 16 of at most 60:
> test-metrics.R: 10 11
> test-metrics.R: 12 13 Optimizing with step length 0.4397.
> test-metrics.R: The log-likelihood improved by 3.0119.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.1e-159
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 17 of at most 60:
> test-miss.CD.R: Convergence test P-value:3.3e-159
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 18 of at most 60:
> test-metrics.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.6225.
> test-metrics.R: The log-likelihood improved by 3.6934.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.6e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 19 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-163
> test-miss.CD.R: 1 2
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 1.0488.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 20 of at most 60:
> test-miss.CD.R: Convergence test P-value:3.5e-156
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 21 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0821.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:2.3e-153
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 22 of at most 60:
> test-miss.R: n=20, density=0.1, missing=0.05
> test-miss.CD.R: Convergence test P-value:1e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 23 of at most 60:
> test-miss.R: Correct estimate = -2.118156 with log-likelihood -120.6883 .
> test-miss.CD.R: Convergence test P-value:1.4e-160
> test-miss.CD.R: 1 2
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: MPLE estimate = -2.118156 with log-likelihood -120.6883 OK.
> test-miss.R:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 24 of at most 60:
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.CD.R: Convergence test P-value:2.1e-158
> test-miss.CD.R: 1 2
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 26 dyads is required.
> test-miss.R: Imputing 3 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 41 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 25 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-150
> test-miss.CD.R: 1
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 49.45267
> test-miss.R: Starting MCMLE Optimization...
> test-miss.CD.R: 2
> test-miss.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.4099.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.5936.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 26 of at most 60:
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.374066
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:4.5e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 27 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.8e-154
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 28 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:8.6e-160
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 29 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.36008
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5
> test-miss.R: 6 7 8 9 10 11 12 Optimizing with step length 0.4981.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.2702.
> test-miss.R: Distance from origin on tolerance region scale: 192.073 (previously 422.077).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.647302
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.9e-157
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 30 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 31 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 17.50766
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 Optimizing with step length 0.7736.
> test-miss.CD.R: Convergence test P-value:1.9e-155
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.0776.
> test-miss.R: Distance from origin on tolerance region scale: 71.38353 (previously 259.1767).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.CD.R: 1
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.95412
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 32 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.5e-156
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 33 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 5.621399
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.3258.
> test-miss.R: Distance from origin on tolerance region scale: 6.488425 (previously 62.9371).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 5 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.070021
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1e-153
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 34 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.CD.R: Convergence test P-value:2.5e-165
> test-miss.CD.R: 1 2
> test-miss.R: New interval = 32.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 1.853909
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0589.
> test-miss.R: Distance from origin on tolerance region scale: 1.17581 (previously 10.81058).
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 35 of at most 60:
> test-miss.R: Test statistic: T^2 = 11.54078, with 1 free parameter(s) and 179.1884 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0008. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.CD.R: Convergence test P-value:3.7e-164
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 36 of at most 60:
> test-miss.R: Dyad-independent submodel MLE has likelihood -120.6883 at:
> test-miss.R: [1] -2.118156 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.CD.R: Convergence test P-value:3.5e-165
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 37 of at most 60:
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-2.133081, 0.000000].
> test-miss.R: Running theta=[-2.132118, 0.000000].
> test-miss.R: Running theta=[-2.131155, 0.000000].
> test-miss.R: Running theta=[-2.130192, 0.000000].
> test-miss.R: Running theta=[-2.129229, 0.000000].
> test-miss.R: Running theta=[-2.128267, 0.000000].
> test-miss.CD.R: Convergence test P-value:3.8e-167
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.127304, 0.000000].
> test-miss.R: Running theta=[-2.126341, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Running theta=[-2.125378, 0.000000].
> test-miss.CD.R: Iteration 38 of at most 60:
> test-miss.R: Running theta=[-2.124415, 0.000000].
> test-miss.R: Running theta=[-2.123452, 0.000000].
> test-miss.R: Running theta=[-2.122489, 0.000000].
> test-miss.CD.R: Convergence test P-value:4.3e-166
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.121526, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 39 of at most 60:
> test-miss.R: Running theta=[-2.120563, 0.000000].
> test-miss.R: Running theta=[-2.1196, 0.0000].
> test-miss.R: Running theta=[-2.118637, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.009575496) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.119005, 0.000000].
> test-miss.R: Running theta=[-2.119968, 0.000000].
> test-miss.R: Running theta=[-2.120931, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.8e-166
> test-miss.R: Running theta=[-2.121894, 0.000000].
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.122857, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 40 of at most 60:
> test-miss.R: Running theta=[-2.12382, 0.00000].
> test-miss.R: Running theta=[-2.124783, 0.000000].
> test-miss.R: Running theta=[-2.125746, 0.000000].
> test-miss.R: Running theta=[-2.126709, 0.000000].
> test-miss.R: Running theta=[-2.127671, 0.000000].
> test-miss.R: Running theta=[-2.128634, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.4e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 41 of at most 60:
> test-miss.R: Running theta=[-2.129597, 0.000000].
> test-miss.R: Running theta=[-2.13056, 0.00000].
> test-miss.R: Running theta=[-2.131523, 0.000000].
> test-miss.R: Running theta=[-2.132486, 0.000000].
> test-miss.R: Running theta=[-2.133449, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.007542247) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132854, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.3e-139
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.131891, 0.000000].
> test-miss.R: Running theta=[-2.130928, 0.000000].
> test-miss.R: Running theta=[-2.129965, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 42 of at most 60:
> test-miss.R: Running theta=[-2.129002, 0.000000].
> test-miss.R: Running theta=[-2.128039, 0.000000].
> test-miss.R: Running theta=[-2.127076, 0.000000].
> test-miss.R: Running theta=[-2.126113, 0.000000].
> test-miss.R: Running theta=[-2.125151, 0.000000].
> test-miss.CD.R: Convergence test P-value:7.6e-160
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.124188, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 43 of at most 60:
> test-miss.R: Running theta=[-2.123225, 0.000000].
> test-miss.R: Running theta=[-2.122262, 0.000000].
> test-miss.R: Running theta=[-2.121299, 0.000000].
> test-miss.R: Running theta=[-2.120336, 0.000000].
> test-miss.R: Running theta=[-2.119373, 0.000000].
> test-miss.CD.R: Convergence test P-value:3e-152
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.11841, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.006188692) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.118778, 0.000000].
> test-miss.R: Running theta=[-2.119741, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 44 of at most 60:
> test-miss.R: Running theta=[-2.120704, 0.000000].
> test-miss.R: Running theta=[-2.121667, 0.000000].
> test-miss.R: Running theta=[-2.12263, 0.00000].
> test-miss.R: Running theta=[-2.123593, 0.000000].
> test-miss.R: Running theta=[-2.124555, 0.000000].
> test-miss.CD.R: Convergence test P-value:3.1e-154
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.125518, 0.000000].
> test-miss.R: Running theta=[-2.126481, 0.000000].
> test-miss.R: Running theta=[-2.127444, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 45 of at most 60:
> test-miss.R: Running theta=[-2.128407, 0.000000].
> test-miss.R: Running theta=[-2.12937, 0.00000].
> test-miss.R: Running theta=[-2.130333, 0.000000].
> test-miss.R: Running theta=[-2.131296, 0.000000].
> test-miss.R: Running theta=[-2.132259, 0.000000].
> test-miss.R: Running theta=[-2.133222, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.9e-150
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005396227) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132626, 0.000000].
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.131664, 0.000000].
> test-miss.R: Running theta=[-2.130701, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 46 of at most 60:
> test-miss.R: Running theta=[-2.129738, 0.000000].
> test-miss.R: Running theta=[-2.128775, 0.000000].
> test-miss.R: Running theta=[-2.127812, 0.000000].
> test-miss.R: Running theta=[-2.126849, 0.000000].
> test-miss.R: Running theta=[-2.125886, 0.000000].
> test-miss.CD.R: Convergence test P-value:8.8e-148
> test-miss.R: Running theta=[-2.124923, 0.000000].
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.12396, 0.00000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 47 of at most 60:
> test-miss.R: Running theta=[-2.122997, 0.000000].
> test-miss.R: Running theta=[-2.122035, 0.000000].
> test-miss.R: Running theta=[-2.121072, 0.000000].
> test-miss.R: Running theta=[-2.120109, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.8e-153
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.119146, 0.000000].
> test-miss.R: Running theta=[-2.118183, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 48 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.8e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 49 of at most 60:
> test-miss.CD.R: Convergence test P-value:6e-154
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 50 of at most 60:
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1728:32768
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 486
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 1.8539 5.6566 0.2566 0.4463
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -8.881 -1.881 2.119 5.119 14.119
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -2.129e-17 1.663e+00 1.067e-01 1.067e-01
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.8807 -1.3807 0.1193 1.1193 4.0693
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. 1.853909e+00 NA
> test-miss.R: test stat. 4.040502e+00 1.632566e+01
> test-miss.R: P-val. 5.333689e-05 7.904382e-05
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 0.50229634
> test-miss.R: Lag 128 0.27968825
> test-miss.R: Lag 192 0.14989734
> test-miss.R: Lag 256 0.08553620
> test-miss.R: Lag 320 0.01488518
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.04999730
> test-miss.R: Lag 128 0.05488745
> test-miss.R: Lag 192 -0.03080175
> test-miss.R: Lag 256 0.03734623
> test-miss.R: Lag 320 0.01289319
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.4658741
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.6413056
> test-miss.R: Joint P-value (lower = worse): 0.5503774
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.CD.R: Convergence test P-value:1.4e-158
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.2553269
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.7984706
> test-miss.R: Joint P-value (lower = worse): 0.5503774 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -2.133563 with log-likelihood -120.7039 OK.
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 51 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.2e-150
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 52 of at most 60:
> test-miss.R: Correct estimate = -1.663142 with log-likelihood -79.82064 .
> test-miss.CD.R: Convergence test P-value:2.2e-161
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 53 of at most 60:
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.CD.R: Convergence test P-value:5.8e-145
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 54 of at most 60:
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.CD.R: Convergence test P-value:5.8e-163
> test-miss.CD.R: 1 2
> test-miss.R: Imputing 8 dyads is required.
> test-miss.R: Imputing 1 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 30 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 55 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 56 of at most 60:
> test-miss.CD.R: Convergence test P-value:2e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 57 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.4e-151
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 58 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.15638
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 6 7 8 Optimizing with step length 0.5368.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 4.3000.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.146333
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.2e-168
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 59 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:3e-159
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 60 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 14.85185
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 3.2552.
> test-miss.R: Distance from origin on tolerance region scale: 64.83604 (previously 323.1391).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.584689
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:6.5e-157
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.600823
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1297.
> test-miss.R: Distance from origin on tolerance region scale: 2.582218 (previously 84.20395).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.684393
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.2386831
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0011.
> test-miss.R: Distance from origin on tolerance region scale: 0.02175454 (previously 2.583022).
> test-miss.R: Test statistic: T^2 = 16.95471, with 1 free parameter(s) and 192.1073 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -79.82064 at:
> test-miss.R: [1] -1.663142 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-1.674863, 0.000000].
> test-miss.R: Running theta=[-1.674107, 0.000000].
> test-miss.R: Running theta=[-1.673351, 0.000000].
> test-miss.R: Running theta=[-1.672594, 0.000000].
> test-miss.R: Running theta=[-1.671838, 0.000000].
> test-miss.R: Running theta=[-1.671082, 0.000000].
> test-miss.R: Running theta=[-1.670326, 0.000000].
> test-miss.R: Running theta=[-1.66957, 0.00000].
> test-miss.R: Running theta=[-1.668813, 0.000000].
> test-miss.R: Running theta=[-1.668057, 0.000000].
> test-miss.R: Running theta=[-1.667301, 0.000000].
> test-miss.R: Running theta=[-1.666545, 0.000000].
> test-miss.R: Running theta=[-1.665789, 0.000000].
> test-miss.R: Running theta=[-1.665033, 0.000000].
> test-miss.R: Running theta=[-1.664276, 0.000000].
> test-miss.R: Running theta=[-1.66352, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005483681) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-1.663809, 0.000000].
> test-miss.R: Running theta=[-1.664565, 0.000000].
> test-miss.R: Running theta=[-1.665321, 0.000000].
> test-miss.R: Running theta=[-1.666078, 0.000000].
> test-miss.R: Running theta=[-1.666834, 0.000000].
> test-miss.R: Running theta=[-1.66759, 0.00000].
> test-miss.R: Running theta=[-1.668346, 0.000000].
> test-miss.R: Running theta=[-1.669102, 0.000000].
> test-miss.R: Running theta=[-1.669858, 0.000000].
> test-miss.R: Running theta=[-1.670615, 0.000000].
> test-miss.R: Running theta=[-1.671371, 0.000000].
> test-miss.R: Running theta=[-1.672127, 0.000000].
> test-miss.R: Running theta=[-1.672883, 0.000000].
> test-miss.R: Running theta=[-1.673639, 0.000000].
> test-miss.R: Running theta=[-1.674396, 0.000000].
> test-miss.R: Running theta=[-1.675152, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.2387 5.2156 0.3346 0.3867
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -9.2346 -4.2346 -0.2346 2.7654 11.7154
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 4.040e-17 1.007e+00 6.463e-02 6.463e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -1.2346 -0.7346 -0.2346 0.7654 2.7154
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.2386831 NA
> test-miss.R: test stat. -0.6087923 0.3706280
> test-miss.R: P-val. 0.5426621 0.5433813
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.000000000
> test-miss.R: Lag 128 0.141730657
> test-miss.R: Lag 256 -0.008044799
> test-miss.R: Lag 384 0.039503814
> test-miss.R: Lag 512 0.016265240
> test-miss.R: Lag 640 0.025525909
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.03837238
> test-miss.R: Lag 128 0.06102163
> test-miss.R: Lag 192 0.01817600
> test-miss.R: Lag 256 -0.07663989
> test-miss.R: Lag 320 -0.02107378
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -1.387683
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.1652336
> test-miss.R: Joint P-value (lower = worse): 0.2706448
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.5702328
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.5685198
> test-miss.R: Joint P-value (lower = worse): 0.2706448 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -1.675241 with log-likelihood -79.81794 OK.
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: Correct estimate = -3.157 with log-likelihood -8.355963 .
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: MPLE estimate = -3.157 with log-likelihood -8.355963 OK.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 2 dyads is required.
> test-miss.R: Imputing 0 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 2 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.942387
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.8416.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.729057
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 0.9012346
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1847.
> test-miss.R: Distance from origin on tolerance region scale: 3.678483 (previously 39.20962).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -3.138904
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.05349794
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0008.
> test-miss.R: Distance from origin on tolerance region scale: 0.01512904 (previously 4.293514).
> test-miss.R: Test statistic: T^2 = 22.84874, with 1 free parameter(s) and 256.603 degrees of freedom.
> test-miss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -8.355963 at:
> test-miss.R: [1] -3.157 0.000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-3.11196, 0.00000].
> test-miss.R: Running theta=[-3.114866, 0.000000].
> test-miss.R: Running theta=[-3.117772, 0.000000].
> test-miss.R: Running theta=[-3.120678, 0.000000].
> test-miss.R: Running theta=[-3.123584, 0.000000].
> test-miss.R: Running theta=[-3.126489, 0.000000].
> test-miss.R: Running theta=[-3.129395, 0.000000].
> test-miss.R: Running theta=[-3.132301, 0.000000].
> test-miss.R: Running theta=[-3.135207, 0.000000].
> test-miss.R: Running theta=[-3.138113, 0.000000].
> test-miss.R: Running theta=[-3.141018, 0.000000].
> test-miss.R: Running theta=[-3.143924, 0.000000].
> test-miss.R: Running theta=[-3.14683, 0.00000].
> test-miss.R: Running theta=[-3.149736, 0.000000].
> test-miss.R: Running theta=[-3.152642, 0.000000].
> test-miss.R: Running theta=[-3.155548, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 3584:65536
> test-miss.R: Thinning interval = 256
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.05350 1.39509 0.08949 0.08949
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.05761 -1.05761 -0.05761 0.94239 2.94239
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 9.838e-19 2.335e-01 1.498e-02 1.763e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -0.05761 -0.05761 -0.05761 -0.05761 0.94239
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.05349794 NA
> test-miss.R: test stat. -0.58650248 0.3476007
> test-miss.R: P-val. 0.55753790 0.5559932
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 256 -0.07640761
> test-miss.R: Lag 512 0.04674441
> test-miss.R: Lag 768 -0.08703223
> test-miss.R: Lag 1024 -0.01273911
> test-miss.R: Lag 1280 0.08918131
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 128 0.01440843
> test-miss.R: Lag 256 -0.06163854
> test-miss.R: Lag 384 -0.05752332
> test-miss.R: Lag 512 0.09381586
> test-miss.R: Lag 640 0.16935966
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 1.222791
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.2214088
> test-miss.R: Joint P-value (lower = worse): 0.530107
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 0.899493
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.3683901
> test-miss.R: Joint P-value (lower = worse): 0.530107 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -3.110507 with log-likelihood -8.357166 OK.
> test-miss.R: Network statistics:
> test-miss.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.R: 0 0 0 0 0 0 0
> test-miss.R: Correct estimate = -2.028148
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Iteration 1 of at most 5:
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 2 of at most 5:
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: 1
> test-miss.R: 2 3 4
> test-miss.R: 5
> test-miss.R: 6
> test-miss.R: 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 3 of at most 5:
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 4 of at most 5:
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6 7 8 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 5 of at most 5:
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6 7 8 9 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
> test-miss.R: MCMLE estimation did not converge after 5 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Using 16 bridges:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3 4 5
> test-miss.R: 6
> test-miss.R: 7 8
> test-miss.R: 9
> test-miss.R: 10
> test-miss.R: 11
> test-miss.R: 12 13
> test-miss.R: 14
> test-miss.R: 15
> test-miss.R: 16
> test-miss.R: .
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-largenetwork.R:
> test-mple-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-offset.R: Obtaining the responsible dyads.
> test-mple-offset.R: Evaluating the predictor and response matrix.
> test-mple-offset.R: Maximizing the pseudolikelihood.
> test-mple-offset.R: Finished MPLE.
> test-mple-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-offset.R: Iteration 1 of at most 60:
> test-mple-offset.R: 1
> test-mple-offset.R: Optimizing with step length 1.0000.
> test-mple-offset.R: The log-likelihood improved by 0.0040.
> test-mple-offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-mple-offset.R: Finished MCMLE.
> test-mple-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-mple-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-target.R: [1] 350 50 250
> test-mple-target.R: Structural check:
> test-mple-target.R: Mean degree: 1.4 .
> test-mple-target.R: Average degree among nodes with degree 2 or higher: 2.25 .
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-target.R: Iteration 1 of at most 60:
> test-networkLite.R: Loading required package: networkLite
> test-mple-cov.R: Estimating Bootstrap Standard Errors using 500 simulated networks.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6 7 8 9 10 11 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4 5 6 7 8 9 10 11 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6
> test-networkLite.R: 7 8 9 10 11 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3 4 5 6 7
> test-networkLite.R: 8 9
> test-networkLite.R: 10 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-nodrop.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: The log-likelihood improved by 0.0005.
> test-nodrop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-networkLite.R: 1 2 3
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: 1 Optimizing with step length 0.2247.
> test-nodrop.R: The log-likelihood improved by 2.2822.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-nodrop.R: 1 Optimizing with step length 0.2817.
> test-nodrop.R: The log-likelihood improved by 2.4734.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.2675.
> test-nodrop.R: The log-likelihood improved by 3.3752.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-nodrop.R: 1 Optimizing with step length 0.3098.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nodrop.R: The log-likelihood improved by 2.4783.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-nonident-test.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonident-test.R: 1 Optimizing with step length 0.8147.
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-networkLite.R: 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonunique-names.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonunique-names.R: Obtaining the responsible dyads.
> test-nonunique-names.R: Evaluating the predictor and response matrix.
> test-nonunique-names.R: Maximizing the pseudolikelihood.
> test-nonunique-names.R: Finished MPLE.
> test-nonunique-names.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonunique-names.R: Iteration 1 of at most 1:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nonunique-names.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-nonunique-names.R: The log-likelihood improved by 0.0084.
> test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-nonunique-names.R: Convergence test p-value: 0.0480. Not converged with 99% confidence; increasing sample size.
> test-nonunique-names.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonunique-names.R: Finished MCMLE.
> test-nonunique-names.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonunique-names.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-nonunique-names.R: Sample statistics summary:
> test-nonunique-names.R:
> test-nonunique-names.R: Iterations = 2304:44032
> test-nonunique-names.R: Thinning interval = 128
> test-nonunique-names.R: Number of chains = 1
> test-nonunique-names.R: Sample size per chain = 327
> test-nonunique-names.R:
> test-nonunique-names.R: 1. Empirical mean and standard deviation for each variable,
> test-nonunique-names.R: plus standard error of the mean:
> test-nonunique-names.R:
> test-nonunique-names.R: Mean SD Naive SE Time-series SE
> test-nonunique-names.R: edgecov.a -0.2171 3.380 0.1869 0.3114
> test-nonunique-names.R: edgecov.a 0.1346 3.565 0.1971 0.4311
> test-nonunique-names.R:
> test-nonunique-names.R: 2. Quantiles for each variable:
> test-nonunique-names.R:
> test-nonunique-names.R: 2.5% 25% 50% 75% 97.5%
> test-nonunique-names.R: edgecov.a -7 -2 0 2 6.85
> test-nonunique-names.R: edgecov.a -7 -2 0 3 7.00
> test-nonunique-names.R:
> test-nonunique-names.R:
> test-nonunique-names.R: Are sample statistics significantly different from observed?
> test-nonunique-names.R: edgecov.a edgecov.a (Omni)
> test-nonunique-names.R: diff. -0.2171254 0.1345566 NA
> test-nonunique-names.R: test stat. -0.6971750 0.3120903 1.2954084
> test-nonunique-names.R: P-val. 0.4856933 0.7549719 0.5307488
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics cross-correlations:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: edgecov.a 1.0000000 0.6853513
> test-nonunique-names.R: edgecov.a 0.6853513 1.0000000
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics auto-correlation:
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: Lag 0 1.000000000 1.00000000
> test-nonunique-names.R: Lag 128 0.592467538 0.65334235
> test-nonunique-names.R: Lag 256 0.298337375 0.41906307
> test-nonunique-names.R: Lag 384 0.082532656 0.22830518
> test-nonunique-names.R: Lag 512 -0.003477022 0.08811653
> test-nonunique-names.R: Lag 640 -0.090771181 0.03701357
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics burn-in diagnostic (Geweke):
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R:
> test-nonunique-names.R: Fraction in 1st window = 0.1
> test-nonunique-names.R: Fraction in 2nd window = 0.5
> test-nonunique-names.R:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: -0.41748721 0.04619135
> test-nonunique-names.R:
> test-nonunique-names.R: Individual P-values (lower = worse):
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: 0.6763221 0.9631577
> test-nonunique-names.R: Joint P-value (lower = worse): 0.8447246
> test-nonunique-names.R:
> test-nonunique-names.R: Note: MCMC diagnostics shown here are from the last round of
> test-nonunique-names.R: simulation, prior to computation of final parameter estimates.
> test-nonunique-names.R: Because the final estimates are refinements of those used for this
> test-nonunique-names.R: simulation run, these diagnostics may understate model performance.
> test-nonunique-names.R: To directly assess the performance of the final model on in-model
> test-nonunique-names.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-nonunique-names.R: GOF=~model).
> test-nonunique-names.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: 1
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-networkLite.R: 1
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: 2 3
> test-networkLite.R: 4
> test-networkLite.R: 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-offsets.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: The log-likelihood improved by 0.6004.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: The log-likelihood improved by 0.0061.
> test-offsets.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-offsets.R: Using 16 bridges: 1
> test-offsets.R: 2
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-offsets.R: 3 4
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-offsets.R: 5
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: 6
> test-offsets.R: 7
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: 8 9
> test-offsets.R: 10
> test-offsets.R: 11
> test-offsets.R: 12
> test-offsets.R: 13
> test-offsets.R: 14 15 16
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.7959.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-parallel.R:
> test-parallel.R: Attaching package: 'statnet.common'
> test-parallel.R:
> test-parallel.R: The following objects are masked from 'package:base':
> test-parallel.R:
> test-parallel.R: attr, order, replace
> test-parallel.R:
> test-parallel.R: parallel test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run.
> test-parallel.R: Skipping OpenMP test. This package installation was built without OpenMP support.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.0207.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: Using 16 bridges: 1
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 2
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 3
> test-offsets.R: 4
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 5
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 6
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 7
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 8
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 9
> test-offsets.R: 10
> test-offsets.R: 11
> test-offsets.R: 12
> test-offsets.R: 13
> test-offsets.R: 14
> test-offsets.R: 15
> test-offsets.R: 16
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-runtime-diags.R: Obtaining the responsible dyads.
> test-runtime-diags.R: Evaluating the predictor and response matrix.
> test-runtime-diags.R: Maximizing the pseudolikelihood.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: Finished MPLE.
> test-runtime-diags.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-runtime-diags.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: 1
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: Optimizing with step length 1.0000.
> test-runtime-diags.R: The log-likelihood improved by 0.0414.
> test-runtime-diags.R: Convergence test p-value: 0.0016. Converged with 99% confidence.
> test-runtime-diags.R: Finished MCMLE.
> test-runtime-diags.R: This model was fit using MCMC. To examine model diagnostics and check
> test-runtime-diags.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: 1 Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate.
> test-scoping.R: Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Using 16 bridges:
> test-scoping.R: 1
> test-scoping.R: 2
> test-scoping.R: 3
> test-scoping.R: 4
> test-scoping.R: 5 6
> test-scoping.R: 7
> test-scoping.R: 8 9
> test-scoping.R: 10 11 12
> test-scoping.R: 13
> test-scoping.R: 14
> test-scoping.R: 15 16
> test-scoping.R: .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-scoping.R: 1
> test-scoping.R: Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges:
> test-scoping.R: 1 2 3
> test-scoping.R: 4 5 6 7 8 9
> test-scoping.R: 10
> test-scoping.R: 11 12
> test-scoping.R: 13 14
> test-scoping.R: 15
> test-scoping.R: 16
> test-scoping.R: .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-shrink-into-CH.R: 1 2 3 4 5 6 7 8 9 10 11 12
> test-shrink-into-CH.R: 13 14
> test-shrink-into-CH.R: 15 16 17
> test-shrink-into-CH.R: 18
> test-shrink-into-CH.R: 19 20 1
> test-shrink-into-CH.R: 2
> test-shrink-into-CH.R: 3 4 5
> test-shrink-into-CH.R: 6
> test-shrink-into-CH.R: 7
> test-shrink-into-CH.R: 8
> test-shrink-into-CH.R: 9 10 11
> test-shrink-into-CH.R: 12 13 14
> test-shrink-into-CH.R: 15
> test-shrink-into-CH.R: 16 17 18 19 20
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-skip.R: Iteration 1 of at most 60:
> test-skip.R: 1 Optimizing with step length 1.0000.
> test-skip.R: The log-likelihood improved by 0.0360.
> test-skip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-skip.R: Finished MCMLE.
> test-skip.R: This model was fit using MCMC. To examine model diagnostics and check
> test-skip.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-snctrl.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-snctrl.R: Obtaining the responsible dyads.
> test-snctrl.R: Evaluating the predictor and response matrix.
> test-snctrl.R: Maximizing the pseudolikelihood.
> test-snctrl.R: Finished MPLE.
> test-snctrl.R: Evaluating log-likelihood at the estimate.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.7009355 0.2208488
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.6617183 0.1405334
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0034.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.5333754 -0.1317716 0.6729982
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.57231795 -0.05712682 0.44962020
> test-stocapprox.R: Phase 3: 1000 iterations
> test-stocapprox.R: (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0007.
> test-stocapprox.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.1e-111
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 1.945.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-44
> test-stocapprox.R: 1 The log-likelihood improved by 0.5962.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:4e-07
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.06364.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.6e-05
> test-stocapprox.R: 1 The log-likelihood improved by 0.04097.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.3e-03
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.01842.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.002083.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.743217 0.112619
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Stochastic Approximation estimate:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.7631980 0.1383531
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Convergence test P-value:1.4e-98
> test-stocapprox.R: 1 The log-likelihood improved by 1.862.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.5e-30
> test-stocapprox.R: 1 The log-likelihood improved by 0.3427.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:3e-09
> test-stocapprox.R: 1 The log-likelihood improved by 0.08204.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.9e-02
> test-stocapprox.R: 1 The log-likelihood improved by 0.01313.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.2e-02
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.009411.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:2.6e-01
> test-stocapprox.R: 1 The log-likelihood improved by 0.005336.
> test-stocapprox.R: Iteration 7 of at most 60:
> test-stocapprox.R: Convergence test P-value:7.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1 The log-likelihood improved by 0.0009177.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0022.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R:
> test-stocapprox.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-stocapprox.R: * 'news(package="ergm.count")' for changes since last version
> test-stocapprox.R: * 'citation("ergm.count")' for citation information
> test-stocapprox.R: * 'https://statnet.org' for help, support, and other information
> test-stocapprox.R:
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 60:
> test-target-offset.R: 1 Optimizing with step length 1.0000.
> test-target-offset.R: The log-likelihood improved by 0.0210.
> test-target-offset.R: Convergence test p-value: 0.0002. Converged with 99% confidence.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-target-offset.R: Using 16 bridges: 1
> test-target-offset.R: 2
> test-target-offset.R: 3 4 5
> test-target-offset.R: 6
> test-target-offset.R: 7
> test-target-offset.R: 8
> test-target-offset.R: 9
> test-target-offset.R: 10 11
> test-target-offset.R: 12
> test-target-offset.R: 13
> test-target-offset.R: 14 15
> test-target-offset.R: 16
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 14336:262144
> test-target-offset.R: Thinning interval = 1024
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 0.55556 4.490 0.2880 0.2880
> test-target-offset.R: degree1 0.04527 2.005 0.1286 0.1286
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges -7 -2.5 0 3 9.00
> test-target-offset.R: degree1 -3 -1.0 0 1 4.95
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Are sample statistics significantly different from observed?
> test-target-offset.R: edges degree1 (Omni)
> test-target-offset.R: diff. 0.55555556 0.04526749 NA
> test-target-offset.R: test stat. 1.92893422 0.35200754 9.455405747
> test-target-offset.R: P-val. 0.05373903 0.72483261 0.009922641
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges degree1
> test-target-offset.R: edges 1.0000000 -0.7250142
> test-target-offset.R: degree1 -0.7250142 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges degree1
> test-target-offset.R: Lag 0 1.000000000 1.000000000
> test-target-offset.R: Lag 1024 -0.061427219 0.030194492
> test-target-offset.R: Lag 2048 0.007868535 0.092075103
> test-target-offset.R: Lag 3072 0.018624816 0.047810603
> test-target-offset.R: Lag 4096 -0.047471894 0.007659206
> test-target-offset.R: Lag 5120 -0.073593205 -0.009870131
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-target-offset.R: Chain 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.4498910 -0.1601093
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.652789 0.872795
> test-target-offset.R: Joint P-value (lower = worse): 0.8380775
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-target-offset.R: Obtaining the responsible dyads.
> test-target-offset.R: Evaluating the predictor and response matrix.
> test-target-offset.R: Maximizing the pseudolikelihood.
> test-target-offset.R: Finished MPLE.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1 Optimizing with step length 0.7297.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 2 of at most 3:
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.6389.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 3 of at most 3:
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.8386.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: MCMLE estimation did not converge after 3 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-target-offset.R: Using 16 bridges: 1 2
> test-target-offset.R: 3 4 5
> test-target-offset.R: 6
> test-target-offset.R: 7
> test-target-offset.R: 8
> test-target-offset.R: 9
> test-target-offset.R: 10 11 12 13 14
> test-target-offset.R: 15
> test-target-offset.R: 16
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 3584:65536
> test-target-offset.R: Thinning interval = 256
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 12.77778 5.118432 0.32835 0.3283476
> test-target-offset.R: gwdegree 0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R: gwdegree.decay 0.01236 0.002961 0.00019 0.0001293
> test-target-offset.R: degree0 -0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges 3.000e+00 9.00000 13.00000 16.00000 23.00000
> test-target-offset.R: gwdegree 1.063e-12 1.00000 1.00000 1.00000 1.00000
> test-target-offset.R: gwdegree.decay 5.955e-03 0.01191 0.01191 0.01489 0.01489
> test-target-offset.R: degree0 -1.000e+00 -1.00000 -1.00000 -1.00000 0.00000
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: edges 1.0000000 0.2709648 0.6049364 -0.2709648
> test-target-offset.R: gwdegree 0.2709648 1.0000000 0.5143643 -1.0000000
> test-target-offset.R: gwdegree.decay 0.6049364 0.5143643 1.0000000 -0.5143643
> test-target-offset.R: degree0 -0.2709648 -1.0000000 -0.5143643 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: Lag 0 1.000000000 1.00000000 1.000000000 1.00000000
> test-target-offset.R: Lag 256 0.006056003 0.02865300 -0.034893817 0.02865300
> test-target-offset.R: Lag 512 0.062813023 -0.07862186 0.002608612 -0.07862186
> test-target-offset.R: Lag 768 -0.089332866 -0.07930006 -0.150790861 -0.07930006
> test-target-offset.R: Lag 1024 -0.091496281 -0.07564135 -0.189123113 -0.07564135
> test-target-offset.R: Lag 1280 0.030192390 0.03461402 0.073975025 0.03461402
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-target-offset.R: Chain 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.05663036 -1.34419649 0.38772965 1.34419649
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.9548397 0.1788849 0.6982161 0.1788849
> test-target-offset.R: Joint P-value (lower = worse): 0.3850888
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: 1 Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0066.
> test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: 1 Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0024.
> test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
Saving _problems/test-term-Offset-21.R
Saving _problems/test-term-Offset-25.R
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-b12nodematch.R: In term 'b1nodematch' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b1dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1factor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1twostar' in package 'ergm': Argument 'base' has been superseded by 'levels2', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: In term 'asymmetric' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b2dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) ideg7+.homophily.group and ideg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) gwodeg.fixed.0 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: In term 'nodeifactor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) odeg7+ and odeg8+ are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) odeg6+.homophily.group, odeg7+.homophily.group, and odeg8+.homophily.group are at their smallest attainabl
> test-term-directed.R: e values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Observed statistic(s) edgecov.YearsTrusted are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-flexible.R: All terms are either offsets or extreme values. No optimization is performed.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Note: Term 'mm(~Grade >= 10, levels = -1)' skipped because it contributes no statistics.
> test-term-gw-sp.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-options.R: 1 Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0013.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Fitting the dyad-independent submodel...
> test-term-options.R: Bridging between the dyad-independent submodel and the full model...
> test-term-options.R: Setting up bridge sampling...
> test-term-options.R: Using 16 bridges: 1
> test-term-options.R: 2
> test-term-options.R: 3
> test-term-options.R: 4
> test-term-options.R: 5 6
> test-term-options.R: 7
> test-term-options.R: 8 9
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: 10
> test-term-options.R: 11
> test-term-options.R: 12
> test-term-options.R: 13 14 15 16
> test-term-options.R: .
> test-term-options.R: Bridging finished.
> test-term-options.R:
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-options.R: 1 Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0003.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'DiscUnif2' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-u-function.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'.
> test-valued-sim.R: mean=1, var=4, corr=0.3
> test-valued-sim.R: eta=(0.192307692307692,0.0824175824175824,0.362637362637363)
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated mean (stats only):0.9964445
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated means (target=1):
> test-valued-sim.R:
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 1.0034478 0.9683903
> test-valued-sim.R: [2,] 1.0302079 NA 0.8923730
> test-valued-sim.R: [3,] 0.6870641 0.7628159 NA
> test-valued-sim.R: Simulated vars (target=4):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 3.863045 4.034701
> test-valued-sim.R: [2,] 3.944388 NA 3.888997
> test-valued-sim.R: [3,] 3.740890 4.202553 NA
> test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3):
> test-valued-sim.R: [1] 0.2781206 0.2324470 0.3420405
> test-valued-sim.R: ==== output='stats', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R:
> test-valued-sim.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-sim.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-sim.R: * 'citation("ergm.count")' for citation information
> test-valued-sim.R: * 'https://statnet.org' for help, support, and other information
> test-valued-sim.R:
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R:
> test-valued-terms.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-terms.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-terms.R: * 'citation("ergm.count")' for citation information
> test-valued-terms.R: * 'https://statnet.org' for help, support, and other information
> test-valued-terms.R:
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• empty test (1):
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-term-Offset.R:19:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `off <- ergm(nw ~ edges + offset(triangle), offset.coef = 0.1)` to throw a message.
── Failure ('test-term-Offset.R:23:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `Off <- ergm(nw ~ edges + Offset(~triangle, which = 1, coef = 0.1))` to throw a message.
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 4.10.1
Flags: --no-vignettes
Check: tests
Result: ERROR
Running ‘requireNamespaceTest.R’ [3s/3s]
Running ‘testthat.R’ [235s/128s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # File tests/testthat.R in package ergm, part of the Statnet suite of packages
> # for network analysis, https://statnet.org .
> #
> # This software is distributed under the GPL-3 license. It is free, open
> # source, and has the attribution requirements (GPL Section 7) at
> # https://statnet.org/attribution .
> #
> # Copyright 2003-2025 Statnet Commons
> ################################################################################
> library(testthat)
> library(ergm)
Loading required package: network
'network' 1.19.0 (2024-12-08), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4.10.1 (2025-08-26), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.
>
> test_check("ergm")
Starting 2 test processes.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2 3 4
> test-basis.R: 5 6
> test-basis.R: 7 8
> test-basis.R: 9 10 11 12
> test-basis.R: 13
> test-basis.R: 14 15
> test-basis.R: 16 .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-basis.R: 3 4
> test-basis.R: 5 6 7
> test-basis.R: 8
> test-basis.R: 9 10
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 11 12 13
> test-basis.R: 14
> test-basis.R: 15 16 .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1 2 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16 .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1 The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1 The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: Attaching package: 'statnet.common'
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: The following objects are masked from 'package:base':
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: attr, order, replace
> test-bridge-target.stats.R:
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1 The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1 The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-bridge-target.stats.R: Unable to match target stats. Using MCMLE estimation.
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-bridge-target.stats.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 1
> test-bridge-target.stats.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: The log-likelihood improved by 0.0219.
> test-bridge-target.stats.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-bridge-target.stats.R: Finished MCMLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: Using 16 bridges: 1 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5 6
> test-bridge-target.stats.R: 7 8
> test-bridge-target.stats.R: 9 10 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 15 16 .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: This model was fit using MCMC. To examine model diagnostics and check
> test-bridge-target.stats.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-basis.R: 2 3 4
> test-basis.R: 5 6
> test-basis.R: 7
> test-bridge-target.stats.R: 2
> test-basis.R: 8 9 10 11
> test-basis.R: 12
> test-basis.R: 13 14 15
> test-basis.R: 16 .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 3
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1 The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1 The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-bridge-target.stats.R: 4
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1 The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1 The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-bridge-target.stats.R: 14
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Setting up bridge sampling...
> test-bridge-target.stats.R: 15
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 16
> test-basis.R: 2
> test-basis.R: 3 4
> test-basis.R: 5
> test-basis.R: 6 7
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10 11
> test-basis.R: 12
> test-basis.R: 13 14 15
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1 The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-bridge-target.stats.R: 2
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1 The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-bridge-target.stats.R: 11
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1 2 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-bridge-target.stats.R: 12
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10 11 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Iteration 1 of at most 60:
> test-checkpointing.R: Saving state in '/tmp/RtmpuC5ozY/file3ac8f02dbf9ae7_001.RData'.
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-checkpointing.R: The log-likelihood improved by 0.0213.
> test-checkpointing.R: Step length converged once. Increasing MCMC sample size.
> test-checkpointing.R: Iteration 2 of at most 60:
> test-checkpointing.R: Saving state in '/tmp/RtmpuC5ozY/file3ac8f02dbf9ae7_002.RData'.
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 9
> test-checkpointing.R: The log-likelihood improved by 0.0238.
> test-bridge-target.stats.R: 10
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-checkpointing.R: Using 16 bridges: 1
> test-checkpointing.R: 2 3 4
> test-checkpointing.R: 5
> test-checkpointing.R: 6
> test-checkpointing.R: 7 8
> test-checkpointing.R: 9 10
> test-checkpointing.R: 11
> test-checkpointing.R: 12 13
> test-checkpointing.R: 14
> test-checkpointing.R: 15 16 .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Resuming from state saved in '/tmp/RtmpuC5ozY/file3ac8f02dbf9ae7_002.RData'.
> test-checkpointing.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 15 16
> test-checkpointing.R: The log-likelihood improved by 0.0145.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-checkpointing.R: Using 16 bridges: 1
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-checkpointing.R: 2
> test-checkpointing.R: 3
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-checkpointing.R: 4 5 6 7
> test-checkpointing.R: 8
> test-checkpointing.R: 9
> test-bridge-target.stats.R: 2
> test-checkpointing.R: 10 11
> test-checkpointing.R: 12 13
> test-checkpointing.R: 14
> test-checkpointing.R: 15
> test-checkpointing.R: 16
> test-bridge-target.stats.R: 3
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R:
> test-constrain-blockdiag.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-constrain-blockdiag.R: * 'news(package="ergm.count")' for changes since last version
> test-constrain-blockdiag.R: * 'citation("ergm.count")' for citation information
> test-constrain-blockdiag.R: * 'https://statnet.org' for help, support, and other information
> test-constrain-blockdiag.R:
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constrain-degrees-edges.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constrain-degrees-edges.R: Iteration 1 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:5.2e-06
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.05768.
> test-constrain-degrees-edges.R: Iteration 2 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:3.7e-03
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.09813.
> test-constrain-degrees-edges.R: Finished CD.
> test-constrain-degrees-edges.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-degrees-edges.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondInDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constrain-dind.R: Iteration 1 of at most 60:
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: 1
> test-constrain-dind.R: Optimizing with step length 1.0000.
> test-constrain-dind.R: The log-likelihood improved by 0.0020.
> test-constrain-dind.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constrain-dind.R: Finished MCMLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Fitting the dyad-independent submodel...
> test-constrain-dind.R: Bridging between the dyad-independent submodel and the full model...
> test-constrain-dind.R: Setting up bridge sampling...
> test-constrain-dind.R: Using 16 bridges: 1
> test-constrain-dind.R: 2
> test-constrain-dind.R: 3
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: 4
> test-constrain-dind.R: 5 6
> test-constrain-dind.R: 7 8
> test-constrain-dind.R: 9
> test-constrain-dind.R: 10 11
> test-constrain-dind.R: 12
> test-constrain-dind.R: 13
> test-constrain-dind.R: 14 15
> test-constrain-dind.R: 16
> test-constrain-dind.R: .
> test-constrain-dind.R: Bridging finished.
> test-constrain-dind.R:
> test-constrain-dind.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-dind.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by 0.0021.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Fitting the dyad-independent submodel...
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Using 16 bridges: 1
> test-drop.R: 2 3 4
> test-drop.R: 5 6
> test-drop.R: 7
> test-drop.R: 8
> test-drop.R: 9
> test-drop.R: 10 11
> test-drop.R: 12
> test-drop.R: 13 14 15 16
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by 0.0010.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Fitting the dyad-independent submodel...
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Using 16 bridges: 1 2 3 4
> test-drop.R: 5
> test-drop.R: 6
> test-drop.R: 7
> test-drop.R: 8
> test-drop.R: 9
> test-drop.R: 10
> test-drop.R: 11 12
> test-drop.R: 13
> test-drop.R: 14
> test-drop.R: 15 16
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R: Evaluating network in model.
> test-drop.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-drop.R: 'ergm:MH_SPDyad'.
> test-drop.R: Initializing model...
> test-drop.R: Model initialized.
> test-drop.R: Using initial method 'MPLE'.
> test-drop.R: Initial parameters provided by caller:
> test-drop.R: None.
> test-drop.R: number of free parameters: 7
> test-drop.R: number of fixed parameters: 0
> test-drop.R: Observed statistic(s) triangle and kstar5 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Fitting initial model.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Density guard set to 10000 from an initial count of 3 edges.
> test-drop.R:
> test-drop.R: Iteration 1 of at most 3 with parameter:
> test-drop.R: edges triangle degree2 kstar2 kstar5
> test-drop.R: 1.313175 -Inf 6.009173 -4.163221 -Inf
> test-drop.R: gwdegree gwdegree.decay
> test-drop.R: -2.860022 1.666671
> test-drop.R: Starting unconstrained MCMC...
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Back from unconstrained MCMC.
> test-drop.R: New interval = 512.
> test-drop.R: Average estimating function values:
> test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay
> test-drop.R: -0.29629630 -0.11934156 -0.11934156 -0.57005199 0.06446661
> test-drop.R: Starting MCMLE Optimization...
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: Using lognormal metric (see control.ergm function).
> test-drop.R: Optimizing loglikelihood
> test-drop.R: Starting MCMC s.e. computation.
> test-drop.R: The log-likelihood improved by 0.0385.
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-drop.R: Estimated covariance matrix of the statistics has nullity 3. Effective parameter number adjusted to 2.
> test-drop.R: Test statistic: T^2 = 21.94136, with 2 free parameter(s) and 240.9959 degrees of freedom.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-constraints.R: All terms are either offsets or extreme values. No optimization is performed.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Setting up bridge sampling...
> test-drop.R: Fitting the dyad-independent submodel...
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges:
> test-constraints.R: 1
> test-constraints.R: 2
> test-drop.R: Dyad-independent submodel MLE has likelihood -11.02185 at:
> test-drop.R: [1] -2.639057 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
> test-drop.R: [8] 0.000000
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Initializing model and proposals...
> test-constraints.R: 3
> test-drop.R: Model and proposals initialized.
> test-drop.R: Using 16 bridges: Running theta=[-1.175967, -Inf, 6.177257,-4.435272, -Inf,-1.386658, 1.950625, 0.000000].
> test-constraints.R: 4
> test-drop.R: Running theta=[-1.270360, -Inf, 5.778724,-4.149126, -Inf,-1.297196, 1.824778, 0.000000].
> test-drop.R: Running theta=[-1.364753, -Inf, 5.380191,-3.862979, -Inf,-1.207735, 1.698931, 0.000000].
> test-constraints.R: 5
> test-drop.R: Running theta=[-1.459146, -Inf, 4.981659,-3.576833, -Inf,-1.118273, 1.573085, 0.000000].
> test-drop.R: Running theta=[-1.553539, -Inf, 4.583126,-3.290686, -Inf,-1.028811, 1.447238, 0.000000].
> test-drop.R: Running theta=[-1.6479316, -Inf, 4.1845933,-3.0045393, -Inf,-0.9393491, 1.3213911, 0.0000000].
> test-drop.R: Running theta=[-1.7423245, -Inf, 3.7860606,-2.7183927, -Inf,-0.8498873, 1.1955444, 0.0000000].
> test-constraints.R: 6
> test-drop.R: Running theta=[-1.8367174, -Inf, 3.3875279,-2.4322461, -Inf,-0.7604255, 1.0696976, 0.0000000].
> test-drop.R: Running theta=[-1.9311104, -Inf, 2.9889952,-2.1460995, -Inf,-0.6709636, 0.9438508, 0.0000000].
> test-drop.R: Running theta=[-2.0255033, -Inf, 2.5904625,-1.8599529, -Inf,-0.5815018, 0.8180040, 0.0000000].
> test-drop.R: Running theta=[-2.1198962, -Inf, 2.1919298,-1.5738063, -Inf,-0.4920400, 0.6921573, 0.0000000].
> test-constraints.R: 7
> test-drop.R: Running theta=[-2.2142891, -Inf, 1.7933971,-1.2876597, -Inf,-0.4025782, 0.5663105, 0.0000000].
> test-drop.R: Running theta=[-2.3086821, -Inf, 1.3948644,-1.0015131, -Inf,-0.3131164, 0.4404637, 0.0000000].
> test-drop.R: Running theta=[-2.4030750, -Inf, 0.9963317,-0.7153665, -Inf,-0.2236545, 0.3146169, 0.0000000].
> test-drop.R: Running theta=[-2.4974679, -Inf, 0.5977990,-0.4292199, -Inf,-0.1341927, 0.1887702, 0.0000000].
> test-drop.R: Running theta=[-2.59186086, -Inf, 0.19926635,-0.14307330, -Inf,-0.04473091, 0.06292339, 0.00000000].
> test-constraints.R: 8
> test-drop.R: .
> test-drop.R: Bridge sampling finished. Collating...
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constraints.R: 9
> test-constraints.R: 10
> test-constraints.R: 11
> test-constraints.R: 12
> test-constraints.R: 13
> test-constraints.R: 14
> test-constraints.R: 15
> test-constraints.R: 16
> test-constraints.R: .
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constraints.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constraints.R: Iteration 1 of at most 60:
> test-constraints.R: Convergence test P-value:1.1e-05
> test-constraints.R: 1 The log-likelihood improved by 0.07919.
> test-constraints.R: Iteration 2 of at most 60:
> test-constraints.R: Convergence test P-value:3.7e-02
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.01687.
> test-constraints.R: Iteration 3 of at most 60:
> test-constraints.R: Convergence test P-value:7e-01
> test-constraints.R: Convergence detected. Stopping.
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.0006029.
> test-constraints.R: Finished CD.
> test-constraints.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constraints.R: Iteration 1 of at most 60:
> test-constraints.R: 1
> test-constraints.R: Optimizing with step length 1.0000.
> test-constraints.R: The log-likelihood improved by 0.0263.
> test-constraints.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constraints.R: Finished MCMLE.
> test-constraints.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges: 1 2
> test-constraints.R: 3
> test-constraints.R: 4
> test-constraints.R: 5 6 7
> test-constraints.R: 8
> test-constraints.R: 9
> test-constraints.R: 10 11
> test-constraints.R: 12
> test-constraints.R: 13
> test-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constraints.R: 14
> test-constraints.R: 15
> test-constraints.R: 16
> test-constraints.R: .
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-constraints.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constraints.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (binary, valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ctriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ctriad (binary)
> test-ergm-term-doc.R: Cyclic triples
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: localtriangle(x) (binary)
> test-ergm-term-doc.R: Triangles within neighborhoods
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1) (binary)
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: opentriad (binary)
> test-ergm-term-doc.R: Open triads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: threetrail(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: threepath(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Three-trails
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: triangle(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: triangles(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangles
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: tripercent(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangle percentage
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ttriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ttriad (binary)
> test-ergm-term-doc.R: Transitive triples
> test-ergm-term-doc.R: Found 31 matching ergm terms:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Definitions for term(s) b2factor :
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the
> test-ergm-term-doc.R: unique values of the attr attribute. Each of these statistics
> test-ergm-term-doc.R: gives the number of times a node with that attribute in the second mode of
> test-ergm-term-doc.R: the network appears in an edge. The second mode of a bipartite network
> test-ergm-term-doc.R: object is sometimes known as the "event" mode.
> test-ergm-term-doc.R: Keywords: bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3factor' were found. Try searching with search='b3factor'instead.
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Found 48 matching ergm terms:
> test-ergm-term-doc.R: B(formula, form) (valued)
> test-ergm-term-doc.R: Wrap binary terms for use in valued models
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Impose a curved structure on term parameters
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Exp(formula) (valued)
> test-ergm-term-doc.R: Exponentiate a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: For(...) (valued)
> test-ergm-term-doc.R: A for operator for terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Label(formula, label, pos) (valued)
> test-ergm-term-doc.R: Modify terms' coefficient names
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Log(formula, log0=-1/sqrt(.Machine$double.eps)) (valued)
> test-ergm-term-doc.R: Take a natural logarithm of a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Prod(formulas, label) (valued)
> test-ergm-term-doc.R: A product (or an arbitrary power combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: S(formula, attrs) (valued)
> test-ergm-term-doc.R: Evaluation on an induced subgraph
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Sum(formulas, label) (valued)
> test-ergm-term-doc.R: A sum (or an arbitrary linear combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiff(attr, pow=1, form="sum") (valued)
> test-ergm-term-doc.R: Absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiffcat(attr, base=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Categorical absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atleast(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values greater than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atmost(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values less than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: attrcov(attr, mat, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate by attribute pairing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalties(threshold=0) (valued)
> test-ergm-term-doc.R: Cyclical ties
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Cyclical weights
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edgecov(x, attrname=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edges (valued)
> test-ergm-term-doc.R: nonzero (valued)
> test-ergm-term-doc.R: Number of edges in the network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: equalto(value=0, tolerance=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values equal to a specific value (within tolerance)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: greaterthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly greater than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ininterval(lower=-Inf, upper=+Inf, open=c(TRUE,TRUE)) (valued)
> test-ergm-term-doc.R: Number of dyads whose values are in an interval
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mm(attrs, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Mixing matrix cells and margins
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mutual(form="min",threshold=0) (valued)
> test-ergm-term-doc.R: Mutuality
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecov(attr, form="sum") (valued)
> test-ergm-term-doc.R: nodemain(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of undirected dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodefactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of in-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeifactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: match(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Uniform homophily and differential homophily
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of out-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeofactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: receiver(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Receiver effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sender(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Sender effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: smallerthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly smaller than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Undirected degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sum(pow=1) (valued)
> test-ergm-term-doc.R: Sum of dyad values (optionally taken to a power)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: transitiveweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Transitive weights
> test-ergm-term-doc.R: Found 4 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DiscUnif(a,b)
> test-ergm-term-doc.R: Discrete Uniform reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: StdNormal
> test-ergm-term-doc.R: Standard Normal reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Unif(a,b)
> test-ergm-term-doc.R: Continuous Uniform reference
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 1 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R: Definitions for term(s) Bernoulli :
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference: Specifies each
> test-ergm-term-doc.R: dyad's baseline distribution to be Bernoulli with probability of
> test-ergm-term-doc.R: the tie being 0.5 . This is the only reference measure used
> test-ergm-term-doc.R: in binary mode.
> test-ergm-term-doc.R: Keywords: binary, discrete, finite, nonnegative
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'Cernoulli' were found. Try searching with search='Cernoulli'instead.
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrees
> test-ergm-term-doc.R: Preserve the receiver degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 16 matching ergm terms:
> test-ergm-term-doc.R: Dyads(fix=NULL, vary=NULL)
> test-ergm-term-doc.R: Constrain fixed or varying dyad-independent terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blockdiag(attr)
> test-ergm-term-doc.R: Block-diagonal structure constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blocks(attr=NULL, levels=NULL, levels2=FALSE, b1levels=NULL, b2levels=NULL)
> test-ergm-term-doc.R: Constrain blocks of dyads defined by mixing type on a vertex attribute.
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise(p01, p10)
> test-ergm-term-doc.R: A soft constraint to adjust the sampled distribution for
> test-ergm-term-doc.R: dyad-level noise with known perturbation probabilities
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: egocentric(attr=NULL, direction="both")
> test-ergm-term-doc.R: Preserve values of dyads incident on vertices with given attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixallbut(free.dyads)
> test-ergm-term-doc.R: Preserve the dyad status in all but the given edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixedas(fixed.dyads, present, absent)
> test-ergm-term-doc.R: Fix specific dyads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: hamming
> test-ergm-term-doc.R: Preserve the hamming distance to the given network (BROKEN: Do NOT Use)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: observed
> test-ergm-term-doc.R: Preserve the observed dyads of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Definitions for term(s) b1degrees :
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks: For bipartite networks, preserve the degree for the first mode of each vertex of the given
> test-ergm-term-doc.R: network, while allowing the degree for the second mode to vary.
> test-ergm-term-doc.R: Keywords: bipartite
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3degrees' were found. Try searching with search='b3degrees'instead.
> test-ergm-term-doc.R: Found 2 matching ergm proposals:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm-term-doc.R: Found 5 matching ergm proposals:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DistRLE
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Found 0 matching ergm proposals:
> test-ergm-term-doc.R: Found 18 matching ergm proposals:
> test-ergm-term-doc.R: BDStratTNT
> test-ergm-term-doc.R: TNT proposal with degree bounds, stratification, and a blocks constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegree
> test-ergm-term-doc.R: MHp for degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeDist
> test-ergm-term-doc.R: MHp for degreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeMix
> test-ergm-term-doc.R: MHp for degree mix constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegree
> test-ergm-term-doc.R: MHp for idegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegreeDist
> test-ergm-term-doc.R: MHp for idegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegree
> test-ergm-term-doc.R: MHp for odegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegreeDist
> test-ergm-term-doc.R: MHp for odegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingConstantEdges
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoiseTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Definitions for proposal(s) randomtoggle :
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Reference: Bernoulli Class: cross-sectional
> test-ergm-term-doc.R: May Enforce: .dyads bd
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No proposals named 'mandomtoggle' were found. Try searching with search='mandomtoggle'instead.
> test-ergm.bridge.llr.R: 2
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-term-doc.R: * 'citation("ergm.count")' for citation information
> test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-term-doc.R:
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergmMPLE.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-ergmMPLE.R: Obtaining the responsible dyads.
> test-ergmMPLE.R: Evaluating the predictor and response matrix.
> test-ergmMPLE.R: Maximizing the pseudolikelihood.
> test-ergmMPLE.R: Finished MPLE.
> test-ergmMPLE.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-gflomiss.R: 1
> test-gflomiss.R: Optimizing with step length 1.0000.
> test-gflomiss.R: The log-likelihood improved by 0.0002.
> test-ergm.bridge.llr.R: 9
> test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: 10
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: 11
> test-gflomiss.R: Using 16 bridges: 1
> test-gflomiss.R: 2
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: 3
> test-gflomiss.R: 4
> test-gflomiss.R: 5
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: 6
> test-gflomiss.R: 7
> test-gflomiss.R: 8
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: 9
> test-gflomiss.R: 10
> test-ergm.bridge.llr.R: 15
> test-gflomiss.R: 11
> test-gflomiss.R: 12
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: 13
> test-gflomiss.R: 14
> test-gflomiss.R: 15
> test-ergm.bridge.llr.R: .
> test-gflomiss.R: 16
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-ergm.bridge.llr.R: 2
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-gflomiss.R: 1 Optimizing with step length 1.0000.
> test-gflomiss.R: The log-likelihood improved by 0.0241.
> test-ergm.bridge.llr.R: 7
> test-gflomiss.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 8
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: 9
> test-gflomiss.R: Using 16 bridges: 1
> test-gflomiss.R: 2
> test-gflomiss.R: 3
> test-ergm.bridge.llr.R: 10
> test-gflomiss.R: 4
> test-gflomiss.R: 5 6
> test-ergm.bridge.llr.R: 11
> test-gflomiss.R: 7
> test-gflomiss.R: 8
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: 9 10
> test-gflomiss.R: 11
> test-gflomiss.R: 12
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: 13
> test-gflomiss.R: 14
> test-gflomiss.R: 15
> test-gflomiss.R: 16
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 14 15
> test-gmonkmiss.R: odegree3 odegree4 odegree5 odegree6
> test-gmonkmiss.R: 1 5 7 5
> test-gmonkmiss.R: idegree2 idegree3 idegree4 idegree5 idegree6 idegree7 idegree8 idegree10
> test-gmonkmiss.R: 3 5 1 3 2 1 1 1
> test-gmonkmiss.R: idegree11
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 16
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.6245.
> test-ergm.bridge.llr.R: 4
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-ergm.bridge.llr.R: 7
> test-gmonkmiss.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-gmonkmiss.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-gmonkmiss.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-gmonkmiss.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-gmonkmiss.R: Iteration 1 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:3.3e-34
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.4205.
> test-gmonkmiss.R: Iteration 2 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.8e-14
> test-ergm.bridge.llr.R: 8
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.1501.
> test-gmonkmiss.R: Iteration 3 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2e-04
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.03536.
> test-gmonkmiss.R: Iteration 4 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.6e-01
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.007343.
> test-gmonkmiss.R: Iteration 5 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2.4e-01
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.00569.
> test-gmonkmiss.R: Iteration 6 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:9.9e-02
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.00904.
> test-gmonkmiss.R: Iteration 7 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:7.6e-01
> test-gmonkmiss.R: Convergence detected. Stopping.
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.001102.
> test-gmonkmiss.R: Finished CD.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 14
> test-gmonkmiss.R: The log-likelihood improved by 0.4514.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0105.
> test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 3
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-ergm.bridge.llr.R: 4
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.7035.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-ergm.bridge.llr.R: 11
> test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 12
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 13
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-metrics.R: 1 Optimizing with step length 0.4613.
> test-metrics.R: The log-likelihood improved by 4.1429.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-metrics.R: 1 Optimizing with step length 0.8364.
> test-metrics.R: The log-likelihood improved by 4.7215.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-ergm.bridge.llr.R: 14
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 1.1346.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-ergm.bridge.llr.R: 15
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1129.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-ergm.bridge.llr.R: 16
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0037.
> test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-miss-dep.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-miss-dep.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.4018.
> test-metrics.R: The log-likelihood improved by 3.2780.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss-dep.R: Convergence test P-value:3.6e-48
> test-miss-dep.R: 1 The log-likelihood improved by 1.676.
> test-miss-dep.R: Iteration 2 of at most 60:
> test-miss-dep.R: Convergence test P-value:1e-13
> test-miss-dep.R: 1 The log-likelihood improved by 0.2714.
> test-miss-dep.R: Iteration 3 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.3e-09
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.1603.
> test-miss-dep.R: Iteration 4 of at most 60:
> test-metrics.R: 1
> test-miss-dep.R: Convergence test P-value:1.3e-02
> test-miss-dep.R: 1
> test-metrics.R: Optimizing with step length 0.6020.
> test-metrics.R: The log-likelihood improved by 3.4584.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: The log-likelihood improved by 0.025.
> test-miss-dep.R: Iteration 5 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.2132.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-miss-dep.R: Convergence test P-value:5e-02
> test-miss-dep.R: 1 The log-likelihood improved by 0.01534.
> test-miss-dep.R: Iteration 6 of at most 60:
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: Convergence test P-value:7.1e-01
> test-miss-dep.R: Convergence detected. Stopping.
> test-miss-dep.R: 1 The log-likelihood improved by 0.0005305.
> test-miss-dep.R: Finished CD.
> test-miss-dep.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0377.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: Post-burnin sample is constant; returning.
> test-miss-dep.R: 1 Optimizing with step length 1.0000.
> test-miss-dep.R: The log-likelihood improved by 0.0015.
> test-miss-dep.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss-dep.R: Finished MCMLE.
> test-miss-dep.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4158.
> test-metrics.R: The log-likelihood improved by 1.9115.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss-dep.R: Using 16 bridges: 1
> test-miss-dep.R: 2
> test-miss-dep.R: 3 4
> test-metrics.R: 1 Optimizing with step length 0.4804.
> test-metrics.R: The log-likelihood improved by 2.5519.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: 5 6 7 8
> test-miss-dep.R: 9
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.9415.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: 10
> test-miss-dep.R: 11
> test-miss-dep.R: 12
> test-miss-dep.R: 13
> test-miss-dep.R: 14
> test-miss-dep.R: 15
> test-miss-dep.R: 16
> test-miss-dep.R: .
> test-miss-dep.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-miss-dep.R: model likelihood is only implemented for dyad-independent constraints
> test-miss-dep.R: at this time. Number of observations is similarly poorly defined. This
> test-miss-dep.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-miss-dep.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-miss-dep.R: distribution and constraints.
> test-miss-dep.R:
> test-miss-dep.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss-dep.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1226.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: n=20, density=0.1, missing=0.1
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:3e-13
> test-miss.CD.R: 1 The log-likelihood improved by 0.2096.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-12
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1974.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:5.8e-11
> test-miss.CD.R: 1 The log-likelihood improved by 0.341.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4e-15
> test-miss.CD.R: 1 The log-likelihood improved by 0.1744.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-16
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6862.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.3e-15
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.3025.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-19
> test-miss.CD.R: 1 The log-likelihood improved by 0.1888.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.3e-17
> test-miss.CD.R: 1 The log-likelihood improved by 0.2862.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.6e-07
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.2007.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 0.3934.
> test-metrics.R: The log-likelihood improved by 4.1457.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.1e-02
> test-miss.CD.R: 1 The log-likelihood improved by 0.03854.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-05
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1259.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1 The log-likelihood improved by 0.0004877.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.8651.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.5e-68
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.7099.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.6e-64
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.5925.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.6e-53
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6327.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.1e-37
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.8025.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-30
> test-miss.CD.R: 1 The log-likelihood improved by 0.583.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-19
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6591.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.2e-04
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.05663.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.4e-01
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.007268.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.3360.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.1e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-54
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.5854.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-metrics.R: 1
> test-miss.CD.R: Convergence test P-value:4.4e-52
> test-miss.CD.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-miss.CD.R: The log-likelihood improved by 0.6164.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.5e-46
> test-miss.CD.R: 1
> test-metrics.R: The log-likelihood improved by 0.0827.
> test-miss.CD.R: The log-likelihood improved by 0.6486.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-32
> test-miss.CD.R: 1
> test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: The log-likelihood improved by 1.361.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.8e-14
> test-miss.CD.R: 1 The log-likelihood improved by 0.379.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-03
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.05118.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.9e-02
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.03478.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.5e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1 The log-likelihood improved by 0.001186.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Network statistics:
> test-miss.CD.R: edges
> test-miss.CD.R: esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.CD.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.CD.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.CD.R: 0 0 0 0 0 0 0
> test-miss.CD.R: Correct estimate = -2.028148
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-283
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.712.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-metrics.R: 1 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 0.3701.
> test-miss.CD.R: Convergence test P-value:1.8e-235
> test-miss.CD.R: 1 2
> test-metrics.R: The log-likelihood improved by 2.3554.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 1.493.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.3e-184
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.167.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-metrics.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.5218.
> test-metrics.R: The log-likelihood improved by 2.9696.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.9e-150
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-147
> test-miss.CD.R: 1 2
> test-metrics.R: 1
> test-metrics.R: 2 3 Optimizing with step length 0.8048.
> test-metrics.R: The log-likelihood improved by 1.8226.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.2705.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.2e-155
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0012.
> test-miss.CD.R: Convergence test P-value:2.8e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: Convergence test p-value: 0.0099. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:1e-154
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:8e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.7e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 13 of at most 60:
> test-metrics.R: 1 2 3
> test-metrics.R: 4 5 6 7
> test-metrics.R: 8 9
> test-metrics.R: 10 11
> test-metrics.R: 12
> test-metrics.R: 13 Optimizing with step length 0.4397.
> test-metrics.R: The log-likelihood improved by 3.0119.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 14 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-162
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 15 of at most 60:
> test-metrics.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.6225.
> test-metrics.R: The log-likelihood improved by 3.6934.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-158
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 16 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.1e-159
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 17 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-miss.CD.R: Convergence test P-value:3.3e-159
> test-metrics.R: The log-likelihood improved by 1.0488.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 18 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.6e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 19 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0821.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:2.5e-163
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 20 of at most 60:
> test-miss.R: n=20, density=0.1, missing=0.05
> test-miss.CD.R: Convergence test P-value:3.5e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 21 of at most 60:
> test-miss.R: Correct estimate = -2.118156 with log-likelihood -120.6883 .
> test-miss.CD.R: Convergence test P-value:2.3e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 22 of at most 60:
> test-miss.R: MPLE estimate = -2.118156 with log-likelihood -120.6883 OK.
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Evaluating network in model.
> test-miss.CD.R: Convergence test P-value:1e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 23 of at most 60:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 26 dyads is required.
> test-miss.R: Imputing 3 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 41 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.4e-160
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 24 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.1e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 25 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.3e-150
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 26 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.5e-158
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 27 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 49.45267
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.4099.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.5936.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.374066
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.8e-154
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 28 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-160
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 29 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.9e-157
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 30 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.36008
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 6 7 8 9
> test-miss.R: 10 11
> test-miss.R: 12
> test-miss.R: Optimizing with step length 0.4981.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.2702.
> test-miss.R: Distance from origin on tolerance region scale: 192.073 (previously 422.077).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.647302
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 31 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.9e-155
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 32 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 17.50766
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3 4 5 Optimizing with step length 0.7736.
> test-miss.CD.R: Convergence test P-value:1.5e-156
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: The log-likelihood improved by 2.0776.
> test-miss.R: Distance from origin on tolerance region scale: 71.38353 (previously 259.1767).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.95412
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 33 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1e-153
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 34 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-165
> test-miss.CD.R: 1 2
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 5.621399
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.3258.
> test-miss.R: Distance from origin on tolerance region scale: 6.488425 (previously 62.9371).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 5 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.070021
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 35 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:3.7e-164
> test-miss.CD.R: 1 2
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 32.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 1.853909
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 36 of at most 60:
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0589.
> test-miss.R: Distance from origin on tolerance region scale: 1.17581 (previously 10.81058).
> test-miss.R: Test statistic: T^2 = 11.54078, with 1 free parameter(s) and 179.1884 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0008. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.CD.R: Convergence test P-value:3.5e-165
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 37 of at most 60:
> test-miss.R: Dyad-independent submodel MLE has likelihood -120.6883 at:
> test-miss.CD.R: Convergence test P-value:3.8e-167
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 38 of at most 60:
> test-miss.R: [1] -2.118156 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-2.133081, 0.000000].
> test-miss.CD.R: Convergence test P-value:4.3e-166
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.132118, 0.000000].
> test-miss.R: Running theta=[-2.131155, 0.000000].
> test-miss.R: Running theta=[-2.130192, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 39 of at most 60:
> test-miss.R: Running theta=[-2.129229, 0.000000].
> test-miss.R: Running theta=[-2.128267, 0.000000].
> test-miss.R: Running theta=[-2.127304, 0.000000].
> test-miss.R: Running theta=[-2.126341, 0.000000].
> test-miss.R: Running theta=[-2.125378, 0.000000].
> test-miss.R: Running theta=[-2.124415, 0.000000].
> test-miss.R: Running theta=[-2.123452, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.8e-166
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 40 of at most 60:
> test-miss.R: Running theta=[-2.122489, 0.000000].
> test-miss.R: Running theta=[-2.121526, 0.000000].
> test-miss.R: Running theta=[-2.120563, 0.000000].
> test-miss.R: Running theta=[-2.1196, 0.0000].
> test-miss.R: Running theta=[-2.118637, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.4e-154
> test-miss.CD.R: 1 2
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.009575496) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.119005, 0.000000].
> test-miss.R: Running theta=[-2.119968, 0.000000].
> test-miss.R: Running theta=[-2.120931, 0.000000].
> test-miss.R: Running theta=[-2.121894, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 41 of at most 60:
> test-miss.R: Running theta=[-2.122857, 0.000000].
> test-miss.R: Running theta=[-2.12382, 0.00000].
> test-miss.R: Running theta=[-2.124783, 0.000000].
> test-miss.R: Running theta=[-2.125746, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.3e-139
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.126709, 0.000000].
> test-miss.R: Running theta=[-2.127671, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 42 of at most 60:
> test-miss.R: Running theta=[-2.128634, 0.000000].
> test-miss.R: Running theta=[-2.129597, 0.000000].
> test-miss.R: Running theta=[-2.13056, 0.00000].
> test-miss.CD.R: Convergence test P-value:7.6e-160
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.131523, 0.000000].
> test-miss.R: Running theta=[-2.132486, 0.000000].
> test-miss.R: Running theta=[-2.133449, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.007542247) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132854, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 43 of at most 60:
> test-miss.R: Running theta=[-2.131891, 0.000000].
> test-miss.R: Running theta=[-2.130928, 0.000000].
> test-miss.R: Running theta=[-2.129965, 0.000000].
> test-miss.R: Running theta=[-2.129002, 0.000000].
> test-miss.R: Running theta=[-2.128039, 0.000000].
> test-miss.CD.R: Convergence test P-value:3e-152
> test-miss.CD.R: 1
> test-miss.R: Running theta=[-2.127076, 0.000000].
> test-miss.R: Running theta=[-2.126113, 0.000000].
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.125151, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 44 of at most 60:
> test-miss.R: Running theta=[-2.124188, 0.000000].
> test-miss.R: Running theta=[-2.123225, 0.000000].
> test-miss.R: Running theta=[-2.122262, 0.000000].
> test-miss.R: Running theta=[-2.121299, 0.000000].
> test-miss.CD.R: Convergence test P-value:3.1e-154
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 45 of at most 60:
> test-miss.R: Running theta=[-2.120336, 0.000000].
> test-miss.R: Running theta=[-2.119373, 0.000000].
> test-miss.R: Running theta=[-2.11841, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.006188692) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.118778, 0.000000].
> test-miss.R: Running theta=[-2.119741, 0.000000].
> test-miss.R: Running theta=[-2.120704, 0.000000].
> test-miss.R: Running theta=[-2.121667, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.9e-150
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 46 of at most 60:
> test-miss.R: Running theta=[-2.12263, 0.00000].
> test-miss.R: Running theta=[-2.123593, 0.000000].
> test-miss.R: Running theta=[-2.124555, 0.000000].
> test-miss.R: Running theta=[-2.125518, 0.000000].
> test-miss.CD.R: Convergence test P-value:8.8e-148
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 47 of at most 60:
> test-miss.R: Running theta=[-2.126481, 0.000000].
> test-miss.R: Running theta=[-2.127444, 0.000000].
> test-miss.R: Running theta=[-2.128407, 0.000000].
> test-miss.R: Running theta=[-2.12937, 0.00000].
> test-miss.R: Running theta=[-2.130333, 0.000000].
> test-miss.R: Running theta=[-2.131296, 0.000000].
> test-miss.R: Running theta=[-2.132259, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.8e-153
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.133222, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005396227) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132626, 0.000000].
> test-miss.R: Running theta=[-2.131664, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 48 of at most 60:
> test-miss.R: Running theta=[-2.130701, 0.000000].
> test-miss.R: Running theta=[-2.129738, 0.000000].
> test-miss.R: Running theta=[-2.128775, 0.000000].
> test-miss.R: Running theta=[-2.127812, 0.000000].
> test-miss.R: Running theta=[-2.126849, 0.000000].
> test-miss.CD.R: Convergence test P-value:4.8e-158
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.125886, 0.000000].
> test-miss.R: Running theta=[-2.124923, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 49 of at most 60:
> test-miss.R: Running theta=[-2.12396, 0.00000].
> test-miss.R: Running theta=[-2.122997, 0.000000].
> test-miss.R: Running theta=[-2.122035, 0.000000].
> test-miss.R: Running theta=[-2.121072, 0.000000].
> test-miss.R: Running theta=[-2.120109, 0.000000].
> test-miss.CD.R: Convergence test P-value:6e-154
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.119146, 0.000000].
> test-miss.R: Running theta=[-2.118183, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 50 of at most 60:
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.4e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 51 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.2e-150
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 52 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.2e-161
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 53 of at most 60:
> test-miss.CD.R: Convergence test P-value:5.8e-145
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 54 of at most 60:
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1728:32768
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 486
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 1.8539 5.6566 0.2566 0.4463
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -8.881 -1.881 2.119 5.119 14.119
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -2.129e-17 1.663e+00 1.067e-01 1.067e-01
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.8807 -1.3807 0.1193 1.1193 4.0693
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. 1.853909e+00 NA
> test-miss.R: test stat. 4.040502e+00 1.632566e+01
> test-miss.R: P-val. 5.333689e-05 7.904382e-05
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 0.50229634
> test-miss.R: Lag 128 0.27968825
> test-miss.R: Lag 192 0.14989734
> test-miss.R: Lag 256 0.08553620
> test-miss.R: Lag 320 0.01488518
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.04999730
> test-miss.R: Lag 128 0.05488745
> test-miss.R: Lag 192 -0.03080175
> test-miss.R: Lag 256 0.03734623
> test-miss.R: Lag 320 0.01289319
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.4658741
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.6413056
> test-miss.R: Joint P-value (lower = worse): 0.5503774
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain
> test-miss.CD.R: Convergence test P-value:5.8e-163
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 55 of at most 60:
> test-miss.R: 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.2553269
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.7984706
> test-miss.R: Joint P-value (lower = worse): 0.5503774 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -2.133563 with log-likelihood -120.7039 OK.
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 56 of at most 60:
> test-miss.R: Correct estimate = -1.663142 with log-likelihood -79.82064 .
> test-miss.CD.R: Convergence test P-value:2e-150
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 57 of at most 60:
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.CD.R: Convergence test P-value:2.4e-151
> test-miss.CD.R: 1 2
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 58 of at most 60:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 8 dyads is required.
> test-miss.R: Imputing 1 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 30 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.2e-168
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 59 of at most 60:
> test-miss.CD.R: Convergence test P-value:3e-159
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 60 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:6.5e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.15638
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 6 7 8 Optimizing with step length 0.5368.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 4.3000.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.146333
> test-miss.R: Starting unconstrained MCMC...
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 14.85185
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 3.2552.
> test-miss.R: Distance from origin on tolerance region scale: 64.83604 (previously 323.1391).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.584689
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.600823
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1297.
> test-miss.R: Distance from origin on tolerance region scale: 2.582218 (previously 84.20395).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.684393
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.2386831
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0011.
> test-miss.R: Distance from origin on tolerance region scale: 0.02175454 (previously 2.583022).
> test-miss.R: Test statistic: T^2 = 16.95471, with 1 free parameter(s) and 192.1073 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -79.82064 at:
> test-miss.R: [1] -1.663142 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-1.674863, 0.000000].
> test-miss.R: Running theta=[-1.674107, 0.000000].
> test-miss.R: Running theta=[-1.673351, 0.000000].
> test-miss.R: Running theta=[-1.672594, 0.000000].
> test-miss.R: Running theta=[-1.671838, 0.000000].
> test-miss.R: Running theta=[-1.671082, 0.000000].
> test-miss.R: Running theta=[-1.670326, 0.000000].
> test-miss.R: Running theta=[-1.66957, 0.00000].
> test-miss.R: Running theta=[-1.668813, 0.000000].
> test-miss.R: Running theta=[-1.668057, 0.000000].
> test-miss.R: Running theta=[-1.667301, 0.000000].
> test-miss.R: Running theta=[-1.666545, 0.000000].
> test-miss.R: Running theta=[-1.665789, 0.000000].
> test-miss.R: Running theta=[-1.665033, 0.000000].
> test-miss.R: Running theta=[-1.664276, 0.000000].
> test-miss.R: Running theta=[-1.66352, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005483681) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-1.663809, 0.000000].
> test-miss.R: Running theta=[-1.664565, 0.000000].
> test-miss.R: Running theta=[-1.665321, 0.000000].
> test-miss.R: Running theta=[-1.666078, 0.000000].
> test-miss.R: Running theta=[-1.666834, 0.000000].
> test-miss.R: Running theta=[-1.66759, 0.00000].
> test-miss.R: Running theta=[-1.668346, 0.000000].
> test-miss.R: Running theta=[-1.669102, 0.000000].
> test-miss.R: Running theta=[-1.669858, 0.000000].
> test-miss.R: Running theta=[-1.670615, 0.000000].
> test-miss.R: Running theta=[-1.671371, 0.000000].
> test-miss.R: Running theta=[-1.672127, 0.000000].
> test-miss.R: Running theta=[-1.672883, 0.000000].
> test-miss.R: Running theta=[-1.673639, 0.000000].
> test-miss.R: Running theta=[-1.674396, 0.000000].
> test-miss.R: Running theta=[-1.675152, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.2387 5.2156 0.3346 0.3867
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -9.2346 -4.2346 -0.2346 2.7654 11.7154
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 4.040e-17 1.007e+00 6.463e-02 6.463e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -1.2346 -0.7346 -0.2346 0.7654 2.7154
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.2386831 NA
> test-miss.R: test stat. -0.6087923 0.3706280
> test-miss.R: P-val. 0.5426621 0.5433813
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.000000000
> test-miss.R: Lag 128 0.141730657
> test-miss.R: Lag 256 -0.008044799
> test-miss.R: Lag 384 0.039503814
> test-miss.R: Lag 512 0.016265240
> test-miss.R: Lag 640 0.025525909
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.03837238
> test-miss.R: Lag 128 0.06102163
> test-miss.R: Lag 192 0.01817600
> test-miss.R: Lag 256 -0.07663989
> test-miss.R: Lag 320 -0.02107378
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -1.387683
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.1652336
> test-miss.R: Joint P-value (lower = worse): 0.2706448
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.5702328
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.5685198
> test-miss.R: Joint P-value (lower = worse): 0.2706448 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -1.675241 with log-likelihood -79.81794 OK.
> test-miss.R: Correct estimate = -3.157 with log-likelihood -8.355963 .
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: MPLE estimate = -3.157 with log-likelihood -8.355963 OK.
> test-miss.R:
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 2 dyads is required.
> test-miss.R: Imputing 0 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 2 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.942387
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.8416.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.729057
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 0.9012346
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1847.
> test-miss.R: Distance from origin on tolerance region scale: 3.678483 (previously 39.20962).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -3.138904
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.05349794
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0008.
> test-miss.R: Distance from origin on tolerance region scale: 0.01512904 (previously 4.293514).
> test-miss.R: Test statistic: T^2 = 22.84874, with 1 free parameter(s) and 256.603 degrees of freedom.
> test-miss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -8.355963 at:
> test-miss.R: [1] -3.157 0.000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-3.11196, 0.00000].
> test-miss.R: Running theta=[-3.114866, 0.000000].
> test-miss.R: Running theta=[-3.117772, 0.000000].
> test-miss.R: Running theta=[-3.120678, 0.000000].
> test-miss.R: Running theta=[-3.123584, 0.000000].
> test-miss.R: Running theta=[-3.126489, 0.000000].
> test-miss.R: Running theta=[-3.129395, 0.000000].
> test-miss.R: Running theta=[-3.132301, 0.000000].
> test-miss.R: Running theta=[-3.135207, 0.000000].
> test-miss.R: Running theta=[-3.138113, 0.000000].
> test-miss.R: Running theta=[-3.141018, 0.000000].
> test-miss.R: Running theta=[-3.143924, 0.000000].
> test-miss.R: Running theta=[-3.14683, 0.00000].
> test-miss.R: Running theta=[-3.149736, 0.000000].
> test-miss.R: Running theta=[-3.152642, 0.000000].
> test-miss.R: Running theta=[-3.155548, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 3584:65536
> test-miss.R: Thinning interval = 256
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.05350 1.39509 0.08949 0.08949
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.05761 -1.05761 -0.05761 0.94239 2.94239
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 9.838e-19 2.335e-01 1.498e-02 1.763e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -0.05761 -0.05761 -0.05761 -0.05761 0.94239
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.05349794 NA
> test-miss.R: test stat. -0.58650248 0.3476007
> test-miss.R: P-val. 0.55753790 0.5559932
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 256 -0.07640761
> test-miss.R: Lag 512 0.04674441
> test-miss.R: Lag 768 -0.08703223
> test-miss.R: Lag 1024 -0.01273911
> test-miss.R: Lag 1280 0.08918131
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 128 0.01440843
> test-miss.R: Lag 256 -0.06163854
> test-miss.R: Lag 384 -0.05752332
> test-miss.R: Lag 512 0.09381586
> test-miss.R: Lag 640 0.16935966
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 1.222791
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.2214088
> test-miss.R: Joint P-value (lower = worse): 0.530107
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 0.899493
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.3683901
> test-miss.R: Joint P-value (lower = worse): 0.530107 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -3.110507 with log-likelihood -8.357166 OK.
> test-miss.R: Network statistics:
> test-miss.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.R: 0 0 0 0 0 0 0
> test-miss.R: Correct estimate = -2.028148
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Iteration 1 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3 4 5
> test-miss.R: 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 2 of at most 5:
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: 1 2
> test-miss.R: 3 4 5
> test-miss.R: 6
> test-miss.R: 7 8
> test-miss.R: 9
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 3 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4 5 6 7 8 9 10
> test-miss.R: 11
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 4 of at most 5:
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6 7 8 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 5 of at most 5:
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: 1
> test-miss.R: 2 3
> test-miss.R: 4 5 6 7 8 9 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
> test-miss.R: MCMLE estimation did not converge after 5 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Using 16 bridges: 1
> test-miss.R: 2
> test-miss.R: 3 4 5
> test-miss.R: 6
> test-miss.R: 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: 10
> test-miss.R: 11
> test-miss.R: 12
> test-miss.R: 13 14 15
> test-miss.R: 16
> test-miss.R: .
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-offset.R: Obtaining the responsible dyads.
> test-mple-offset.R: Evaluating the predictor and response matrix.
> test-mple-offset.R: Maximizing the pseudolikelihood.
> test-mple-offset.R: Finished MPLE.
> test-mple-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-offset.R: Iteration 1 of at most 60:
> test-mple-offset.R: 1
> test-mple-offset.R: Optimizing with step length 1.0000.
> test-mple-offset.R: The log-likelihood improved by 0.0040.
> test-mple-offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-mple-offset.R: Finished MCMLE.
> test-mple-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-mple-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-target.R: [1] 350 50 250
> test-mple-target.R: Structural check:
> test-mple-target.R: Mean degree: 1.4 .
> test-mple-target.R: Average degree among nodes with degree 2 or higher: 2.25 .
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-target.R: Iteration 1 of at most 60:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: Loading required package: networkLite
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Estimating Bootstrap Standard Errors using 500 simulated networks.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3 4 5 6
> test-networkLite.R: 7 8
> test-networkLite.R: 9 10 11 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 2 3 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7 8
> test-networkLite.R: 9
> test-networkLite.R: 10 11 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7 8 9 10 11
> test-networkLite.R: 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3 4 5 6 7 8 9 10
> test-networkLite.R: 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 1.0000.
> test-nodrop.R: The log-likelihood improved by 0.0005.
> test-nodrop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-nodrop.R: 1 Optimizing with step length 0.2247.
> test-nodrop.R: The log-likelihood improved by 2.2822.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-networkLite.R: Convergence test p-value: 0.0019.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.2817.
> test-nodrop.R: The log-likelihood improved by 2.4734.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.2675.
> test-nodrop.R: The log-likelihood improved by 3.3752.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nodrop.R: 1 Optimizing with step length 0.3098.
> test-nodrop.R: The log-likelihood improved by 2.4783.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-networkLite.R: 1
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-networkLite.R: 1
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-nonident-test.R: 1 Optimizing with step length 1.0000.
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: 1 Optimizing with step length 0.8147.
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1 2
> test-networkLite.R: 3 4 5 6 7 8 9 10 11 12 13 14
> test-networkLite.R: 15
> test-networkLite.R: 16
> test-networkLite.R: 17 18 19 20 21
> test-networkLite.R: 22 23 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nonunique-names.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonunique-names.R: Obtaining the responsible dyads.
> test-nonunique-names.R: Evaluating the predictor and response matrix.
> test-nonunique-names.R: Maximizing the pseudolikelihood.
> test-nonunique-names.R: Finished MPLE.
> test-nonunique-names.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonunique-names.R: Iteration 1 of at most 1:
> test-networkLite.R: 1 2
> test-networkLite.R: 3
> test-networkLite.R: 4 5
> test-networkLite.R: 6
> test-networkLite.R: 7 8 9
> test-networkLite.R: 10
> test-networkLite.R: 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-nonunique-names.R: 1 Optimizing with step length 1.0000.
> test-nonunique-names.R: The log-likelihood improved by 0.0084.
> test-nonunique-names.R: Convergence test p-value: 0.0480. Not converged with 99% confidence; increasing sample size.
> test-nonunique-names.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonunique-names.R: Finished MCMLE.
> test-nonunique-names.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonunique-names.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-nonunique-names.R: Sample statistics summary:
> test-nonunique-names.R:
> test-nonunique-names.R: Iterations = 2304:44032
> test-nonunique-names.R: Thinning interval = 128
> test-nonunique-names.R: Number of chains = 1
> test-nonunique-names.R: Sample size per chain = 327
> test-nonunique-names.R:
> test-nonunique-names.R: 1. Empirical mean and standard deviation for each variable,
> test-nonunique-names.R: plus standard error of the mean:
> test-nonunique-names.R:
> test-nonunique-names.R: Mean SD Naive SE Time-series SE
> test-nonunique-names.R: edgecov.a -0.2171 3.380 0.1869 0.3114
> test-nonunique-names.R: edgecov.a 0.1346 3.565 0.1971 0.4311
> test-nonunique-names.R:
> test-nonunique-names.R: 2. Quantiles for each variable:
> test-nonunique-names.R:
> test-nonunique-names.R: 2.5% 25% 50% 75% 97.5%
> test-nonunique-names.R: edgecov.a -7 -2 0 2 6.85
> test-nonunique-names.R: edgecov.a -7 -2 0 3 7.00
> test-nonunique-names.R:
> test-nonunique-names.R:
> test-nonunique-names.R: Are sample statistics significantly different from observed?
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nonunique-names.R: edgecov.a edgecov.a (Omni)
> test-nonunique-names.R: diff. -0.2171254 0.1345566 NA
> test-nonunique-names.R: test stat. -0.6971750 0.3120903 1.2954084
> test-nonunique-names.R: P-val. 0.4856933 0.7549719 0.5307488
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics cross-correlations:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: edgecov.a 1.0000000 0.6853513
> test-nonunique-names.R: edgecov.a 0.6853513 1.0000000
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics auto-correlation:
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: Lag 0 1.000000000 1.00000000
> test-nonunique-names.R: Lag 128 0.592467538 0.65334235
> test-nonunique-names.R: Lag 256 0.298337375 0.41906307
> test-nonunique-names.R: Lag 384 0.082532656 0.22830518
> test-nonunique-names.R: Lag 512 -0.003477022 0.08811653
> test-nonunique-names.R: Lag 640 -0.090771181 0.03701357
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics burn-in diagnostic (Geweke):
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R:
> test-nonunique-names.R: Fraction in 1st window = 0.1
> test-nonunique-names.R: Fraction in 2nd window = 0.5
> test-nonunique-names.R:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: -0.41748721 0.04619135
> test-nonunique-names.R:
> test-nonunique-names.R: Individual P-values (lower = worse):
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: 0.6763221 0.9631577
> test-nonunique-names.R: Joint P-value (lower = worse): 0.8447246
> test-nonunique-names.R:
> test-nonunique-names.R: Note: MCMC diagnostics shown here are from the last round of
> test-nonunique-names.R: simulation, prior to computation of final parameter estimates.
> test-nonunique-names.R: Because the final estimates are refinements of those used for this
> test-nonunique-names.R: simulation run, these diagnostics may understate model performance.
> test-nonunique-names.R: To directly assess the performance of the final model on in-model
> test-nonunique-names.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-nonunique-names.R: GOF=~model).
> test-nonunique-names.R:
> test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.6004.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4 5
> test-networkLite.R: 6 7 8
> test-networkLite.R: 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-offsets.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.0061.
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-offsets.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-offsets.R: Using 16 bridges: 1
> test-offsets.R: 2
> test-offsets.R: 3
> test-offsets.R: 4
> test-offsets.R: 5
> test-offsets.R: 6
> test-offsets.R: 7
> test-offsets.R: 8
> test-offsets.R: 9
> test-offsets.R: 10
> test-offsets.R: 11
> test-offsets.R: 12 13
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: 14 15
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-offsets.R: 16 .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.7959.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-parallel.R:
> test-parallel.R: Attaching package: 'statnet.common'
> test-parallel.R:
> test-parallel.R: The following objects are masked from 'package:base':
> test-parallel.R:
> test-parallel.R: attr, order, replace
> test-parallel.R:
> test-parallel.R: parallel test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run.
> test-parallel.R: Skipping OpenMP test. This package installation was built without OpenMP support.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-offsets.R: The log-likelihood improved by 0.0207.
> test-offsets.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-offsets.R: Using 16 bridges: 1
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 2
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 3
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 4
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 5
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 6
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 7
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 8
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 9
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 10
> test-offsets.R: 11
> test-offsets.R: 12
> test-offsets.R: 13
> test-offsets.R: 14
> test-offsets.R: 15
> test-offsets.R: 16
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-runtime-diags.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-runtime-diags.R: Obtaining the responsible dyads.
> test-runtime-diags.R: Evaluating the predictor and response matrix.
> test-runtime-diags.R: Maximizing the pseudolikelihood.
> test-runtime-diags.R: Finished MPLE.
> test-runtime-diags.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-runtime-diags.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: 1
> test-runtime-diags.R: Optimizing with step length 1.0000.
> test-runtime-diags.R: The log-likelihood improved by 0.0414.
> test-runtime-diags.R: Convergence test p-value: 0.0016. Converged with 99% confidence.
> test-runtime-diags.R: Finished MCMLE.
> test-runtime-diags.R: This model was fit using MCMC. To examine model diagnostics and check
> test-runtime-diags.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-scoping.R: 1 Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate.
> test-scoping.R: Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges: 1
> test-scoping.R: 2
> test-scoping.R: 3
> test-scoping.R: 4
> test-scoping.R: 5
> test-scoping.R: 6 7 8 9
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: 10 11 12 13
> test-scoping.R: 14
> test-scoping.R: 15
> test-scoping.R: 16
> test-scoping.R: .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: 1 Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges: 1
> test-scoping.R: 2 3 4 5
> test-scoping.R: 6
> test-scoping.R: 7
> test-scoping.R: 8
> test-scoping.R: 9 10 11 12
> test-scoping.R: 13
> test-scoping.R: 14 15
> test-scoping.R: 16
> test-scoping.R: .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-shrink-into-CH.R: 1 2 3 4 5 6 7
> test-shrink-into-CH.R: 8
> test-shrink-into-CH.R: 9
> test-shrink-into-CH.R: 10
> test-shrink-into-CH.R: 11
> test-shrink-into-CH.R: 12 13 14 15 16 17
> test-shrink-into-CH.R: 18 19
> test-shrink-into-CH.R: 20
> test-shrink-into-CH.R: 1
> test-shrink-into-CH.R: 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
> test-shrink-into-CH.R: 20
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R:
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-skip.R: Iteration 1 of at most 60:
> test-skip.R: 1 Optimizing with step length 1.0000.
> test-skip.R: The log-likelihood improved by 0.0360.
> test-skip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-skip.R: Finished MCMLE.
> test-skip.R: This model was fit using MCMC. To examine model diagnostics and check
> test-skip.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-snctrl.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-snctrl.R: Obtaining the responsible dyads.
> test-snctrl.R: Evaluating the predictor and response matrix.
> test-snctrl.R: Maximizing the pseudolikelihood.
> test-snctrl.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-snctrl.R: Evaluating log-likelihood at the estimate.
> test-snctrl.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.7009355 0.2208488
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.6617183 0.1405334
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0034.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.5333754 -0.1317716 0.6729982
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.57231795 -0.05712682 0.44962020
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0007.
> test-stocapprox.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.1e-111
> test-stocapprox.R: 1 The log-likelihood improved by 1.945.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-44
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.5962.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:4e-07
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.06364.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.6e-05
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.04097.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.3e-03
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.01842.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.743217 0.112619
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: The log-likelihood improved by 0.002083.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.7631980 0.1383531
> test-stocapprox.R: Phase 3: 1000 iterations
> test-stocapprox.R: (interval=1024)
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-98
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 1.862.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.5e-30
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.3427.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:3e-09
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.08204.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.9e-02
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.01313.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.2e-02
> test-stocapprox.R: 1 The log-likelihood improved by 0.009411.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:2.6e-01
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.005336.
> test-stocapprox.R: Iteration 7 of at most 60:
> test-stocapprox.R: Convergence test P-value:7.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: The log-likelihood improved by 0.0009177.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0022.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R:
> test-stocapprox.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-stocapprox.R: * 'news(package="ergm.count")' for changes since last version
> test-stocapprox.R: * 'citation("ergm.count")' for citation information
> test-stocapprox.R: * 'https://statnet.org' for help, support, and other information
> test-stocapprox.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 1.0000.
> test-target-offset.R: The log-likelihood improved by 0.0210.
> test-target-offset.R: Convergence test p-value: 0.0002. Converged with 99% confidence.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-target-offset.R: Using 16 bridges: 1
> test-target-offset.R: 2
> test-target-offset.R: 3
> test-target-offset.R: 4
> test-target-offset.R: 5
> test-target-offset.R: 6
> test-target-offset.R: 7
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 8
> test-target-offset.R: 9 10 11 12 13
> test-target-offset.R: 14
> test-target-offset.R: 15
> test-target-offset.R: 16
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 14336:262144
> test-target-offset.R: Thinning interval = 1024
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 0.55556 4.490 0.2880 0.2880
> test-target-offset.R: degree1 0.04527 2.005 0.1286 0.1286
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges -7 -2.5 0 3 9.00
> test-target-offset.R: degree1 -3 -1.0 0 1 4.95
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Are sample statistics significantly different from observed?
> test-target-offset.R: edges degree1 (Omni)
> test-target-offset.R: diff. 0.55555556 0.04526749 NA
> test-target-offset.R: test stat. 1.92893422 0.35200754 9.455405747
> test-target-offset.R: P-val. 0.05373903 0.72483261 0.009922641
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges degree1
> test-target-offset.R: edges 1.0000000 -0.7250142
> test-target-offset.R: degree1 -0.7250142 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges degree1
> test-target-offset.R: Lag 0 1.000000000 1.000000000
> test-target-offset.R: Lag 1024 -0.061427219 0.030194492
> test-target-offset.R: Lag 2048 0.007868535 0.092075103
> test-target-offset.R: Lag 3072 0.018624816 0.047810603
> test-target-offset.R: Lag 4096 -0.047471894 0.007659206
> test-target-offset.R: Lag 5120 -0.073593205 -0.009870131
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-target-offset.R: Chain
> test-target-offset.R: 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.4498910 -0.1601093
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.652789 0.872795
> test-target-offset.R: Joint P-value (lower = worse): 0.8380775
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-target-offset.R: Obtaining the responsible dyads.
> test-target-offset.R: Evaluating the predictor and response matrix.
> test-target-offset.R: Maximizing the pseudolikelihood.
> test-target-offset.R: Finished MPLE.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 3:
> test-target-offset.R: 1 Optimizing with step length 0.7297.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 2 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.6389.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 3 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1 Optimizing with step length 0.8386.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: MCMLE estimation did not converge after 3 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-target-offset.R: Using 16 bridges: 1
> test-target-offset.R: 2
> test-target-offset.R: 3
> test-target-offset.R: 4 5
> test-target-offset.R: 6
> test-target-offset.R: 7
> test-target-offset.R: 8
> test-target-offset.R: 9
> test-target-offset.R: 10
> test-target-offset.R: 11
> test-target-offset.R: 12
> test-target-offset.R: 13
> test-target-offset.R: 14
> test-target-offset.R: 15
> test-target-offset.R: 16
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 3584:65536
> test-target-offset.R: Thinning interval = 256
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 12.77778 5.118432 0.32835 0.3283476
> test-target-offset.R: gwdegree 0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R: gwdegree.decay 0.01236 0.002961 0.00019 0.0001293
> test-target-offset.R: degree0 -0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges 3.000e+00 9.00000 13.00000 16.00000 23.00000
> test-target-offset.R: gwdegree 1.063e-12 1.00000 1.00000 1.00000 1.00000
> test-target-offset.R: gwdegree.decay 5.955e-03 0.01191 0.01191 0.01489 0.01489
> test-target-offset.R: degree0 -1.000e+00 -1.00000 -1.00000 -1.00000 0.00000
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: edges 1.0000000 0.2709648 0.6049364 -0.2709648
> test-target-offset.R: gwdegree 0.2709648 1.0000000 0.5143643 -1.0000000
> test-target-offset.R: gwdegree.decay 0.6049364 0.5143643 1.0000000 -0.5143643
> test-target-offset.R: degree0 -0.2709648 -1.0000000 -0.5143643 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: Lag 0 1.000000000 1.00000000 1.000000000 1.00000000
> test-target-offset.R: Lag 256 0.006056003 0.02865300 -0.034893817 0.02865300
> test-target-offset.R: Lag 512 0.062813023 -0.07862186 0.002608612 -0.07862186
> test-target-offset.R: Lag 768 -0.089332866 -0.07930006 -0.150790861 -0.07930006
> test-target-offset.R: Lag 1024 -0.091496281 -0.07564135 -0.189123113 -0.07564135
> test-target-offset.R: Lag 1280 0.030192390 0.03461402 0.073975025 0.03461402
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-target-offset.R: Chain 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.05663036 -1.34419649 0.38772965 1.34419649
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.9548397 0.1788849 0.6982161 0.1788849
> test-target-offset.R: Joint P-value (lower = worse): 0.3850888
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-term-Offset.R: 1 Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0066.
> test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-term-Offset.R: 1 Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0024.
> test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
Saving _problems/test-term-Offset-21.R
Saving _problems/test-term-Offset-25.R
> test-term-b12nodematch.R: In term 'b1nodematch' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: In term 'asymmetric' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b1dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1factor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) ideg7+.homophily.group and ideg8+.homophily.group are at their smallest attainable values. Their coefficie
> test-term-directed.R: nts will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) gwodeg.fixed.0 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1twostar' in package 'ergm': Argument 'base' has been superseded by 'levels2', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b2dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: In term 'nodeifactor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) odeg7+ and odeg8+ are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) odeg6+.homophily.group, odeg7+.homophily.group, and odeg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Observed statistic(s) edgecov.YearsTrusted are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-flexible.R: All terms are either offsets or extreme values. No optimization is performed.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-mm.R: Note: Term 'mm(~Grade >= 10, levels = -1)' skipped because it contributes no statistics.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: 1 Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0013.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-term-options.R: Bridging between the dyad-independent submodel and the full model...
> test-term-options.R: Setting up bridge sampling...
> test-term-options.R: Using 16 bridges: 1
> test-term-options.R: 2 3
> test-term-options.R: 4 5 6 7 8
> test-term-options.R: 9
> test-term-options.R: 10
> test-term-options.R: 11
> test-term-options.R: 12
> test-term-options.R: 13
> test-term-options.R: 14
> test-term-options.R: 15
> test-term-options.R: 16 .
> test-term-options.R: Bridging finished.
> test-term-options.R:
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-options.R: 1 Optimizing with step length 1.0000.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: The log-likelihood improved by 0.0003.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'DiscUnif2' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-u-function.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'.
> test-valued-sim.R: mean=1, var=4, corr=0.3
> test-valued-sim.R: eta=(0.192307692307692,0.0824175824175824,0.362637362637363)
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated mean (stats only):1.035811
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated means (target=1):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 1.249752 1.0694978
> test-valued-sim.R: [2,] 1.1683346 NA 0.9908037
> test-valued-sim.R: [3,] 0.8883529 1.073375 NA
> test-valued-sim.R: Simulated vars (target=4):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 3.671528 3.714276
> test-valued-sim.R: [2,] 3.813191 NA 4.279456
> test-valued-sim.R: [3,] 3.771838 4.489522 NA
> test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3):
> test-valued-sim.R: [1] 0.2693591 0.2503898 0.3282326
> test-valued-sim.R: ==== output='stats', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R:
> test-valued-sim.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-sim.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-sim.R: * 'c
> test-valued-sim.R: itation("ergm.count")' for citation information
> test-valued-sim.R: * 'https://statnet.org' for help, support, and other information
> test-valued-sim.R:
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R:
> test-valued-terms.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-terms.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-terms.R: * 'citation("ergm.count")' for citation information
> test-valued-terms.R: * 'https://statnet.org' for help, support, and other information
> test-valued-terms.R:
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• empty test (1):
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-term-Offset.R:19:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `off <- ergm(nw ~ edges + offset(triangle), offset.coef = 0.1)` to throw a message.
── Failure ('test-term-Offset.R:23:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `Off <- ergm(nw ~ edges + Offset(~triangle, which = 1, coef = 0.1))` to throw a message.
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 4.10.1
Check: tests
Result: ERROR
Running ‘requireNamespaceTest.R’
Running ‘testthat.R’ [563s/403s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # File tests/testthat.R in package ergm, part of the Statnet suite of packages
> # for network analysis, https://statnet.org .
> #
> # This software is distributed under the GPL-3 license. It is free, open
> # source, and has the attribution requirements (GPL Section 7) at
> # https://statnet.org/attribution .
> #
> # Copyright 2003-2025 Statnet Commons
> ################################################################################
> library(testthat)
> library(ergm)
Loading required package: network
'network' 1.19.0 (2024-12-08), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4.10.1 (2025-08-26), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.
>
> test_check("ergm")
Starting 2 test processes.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges:
> test-basis.R: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011.
> test-basis.R: Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges:
> test-basis.R: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: Attaching package: 'statnet.common'
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: The following objects are masked from 'package:base':
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: attr, order, replace
> test-bridge-target.stats.R:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-basis.R: Convergence test p-value: 0.0001.
> test-basis.R: Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-bridge-target.stats.R:
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: Unable to match target stats. Using MCMLE estimation.
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-bridge-target.stats.R: Iteration 1 of at most 60:
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network
> test-basis.R: satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-bridge-target.stats.R: 1
> test-bridge-target.stats.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: The log-likelihood improved by 0.0219.
> test-bridge-target.stats.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-bridge-target.stats.R: Finished MCMLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: This model was fit using MCMC. To examine model diagnostics and check
> test-bridge-target.stats.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges:
> test-bridge-target.stats.R: 1
> test-basis.R: 1
> test-bridge-target.stats.R: 2
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 3
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 4
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-bridge-target.stats.R: 5
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 6
> test-basis.R: Using 16 bridges:
> test-basis.R: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-bridge-target.stats.R: 7
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-bridge-target.stats.R: 8
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 9
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 10
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-bridge-target.stats.R: 11
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-bridge-target.stats.R: 12
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 8
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-bridge-target.stats.R: 9
> test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 10
> test-basis.R: Using 16 bridges:
> test-basis.R: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-bridge-target.stats.R: 11
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-bridge-target.stats.R: 12
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Iteration 1 of at most 60:
> test-checkpointing.R: Saving state in '/tmp/RtmpTnA6fB/working_dir/RtmpSkymLU/file262ac116bafa87_001.RData'.
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges:
> test-bridge-target.stats.R: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-checkpointing.R: 1
> test-bridge-target.stats.R: 5
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-checkpointing.R: The log-likelihood improved by 0.0213.
> test-checkpointing.R: Step length converged once. Increasing MCMC sample size.
> test-checkpointing.R: Iteration 2 of at most 60:
> test-checkpointing.R: Saving state in '/tmp/RtmpTnA6fB/working_dir/RtmpSkymLU/file262ac116bafa87_002.RData'.
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 13
> test-checkpointing.R: The log-likelihood improved by 0.0238.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-bridge-target.stats.R: 14
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: 15
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-bridge-target.stats.R: 16
> test-checkpointing.R: Using 16 bridges:
> test-checkpointing.R: 1
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-checkpointing.R: 2
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-checkpointing.R: 3
> test-checkpointing.R: 4
> test-checkpointing.R: 5
> test-checkpointing.R: 6
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-checkpointing.R: 7
> test-checkpointing.R: 8
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-checkpointing.R: 9
> test-checkpointing.R: 10
> test-checkpointing.R: 11
> test-checkpointing.R: 12
> test-checkpointing.R: 13
> test-checkpointing.R: 14
> test-bridge-target.stats.R: 2
> test-checkpointing.R: 15
> test-checkpointing.R: 16
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 3
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Resuming from state saved in '/tmp/RtmpTnA6fB/working_dir/RtmpSkymLU/file262ac116bafa87_002.RData'.
> test-checkpointing.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 10
> test-checkpointing.R: The log-likelihood improved by 0.0145.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: 11
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-checkpointing.R: Using 16 bridges:
> test-checkpointing.R: 1
> test-bridge-target.stats.R: 12
> test-checkpointing.R: 2
> test-checkpointing.R: 3
> test-checkpointing.R: 4
> test-checkpointing.R: 5
> test-checkpointing.R: 6
> test-checkpointing.R: 7
> test-bridge-target.stats.R: 13
> test-checkpointing.R: 8
> test-checkpointing.R: 9
> test-checkpointing.R: 10
> test-checkpointing.R: 11
> test-bridge-target.stats.R: 14
> test-checkpointing.R: 12
> test-checkpointing.R: 13
> test-checkpointing.R: 14
> test-checkpointing.R: 15
> test-checkpointing.R: 16
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constrain-degrees-edges.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constrain-degrees-edges.R: Iteration 1 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:5.2e-06
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.05768.
> test-constrain-degrees-edges.R: Iteration 2 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:3.7e-03
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.09813.
> test-constrain-degrees-edges.R: Finished CD.
> test-constrain-degrees-edges.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-degrees-edges.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondInDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R:
> test-constrain-blockdiag.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-constrain-blockdiag.R: * 'news(package="ergm.count")' for changes since last version
> test-constrain-blockdiag.R: * 'citation("ergm.count")' for citation information
> test-constrain-blockdiag.R: * 'https://statnet.org' for help, support, and other information
> test-constrain-blockdiag.R:
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constrain-dind.R: Iteration 1 of at most 60:
> test-constrain-dind.R: 1
> test-constrain-dind.R: Optimizing with step length 1.0000.
> test-constrain-dind.R: The log-likelihood improved by 0.0020.
> test-constrain-dind.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constrain-dind.R: Finished MCMLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Fitting the dyad-independent submodel...
> test-constrain-dind.R: Bridging between the dyad-independent submodel and the full model...
> test-constrain-dind.R: Setting up bridge sampling...
> test-constrain-dind.R: Using 16 bridges:
> test-constrain-dind.R: 1
> test-constrain-dind.R: 2
> test-constrain-dind.R: 3
> test-constrain-dind.R: 4
> test-constrain-dind.R: 5
> test-constrain-dind.R: 6
> test-constrain-dind.R: 7
> test-constrain-dind.R: 8
> test-constrain-dind.R: 9
> test-constrain-dind.R: 10
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: 11
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constrain-dind.R: 12
> test-constrain-dind.R: 13
> test-constrain-dind.R: 14
> test-constrain-dind.R: 15
> test-constrain-dind.R: 16
> test-constrain-dind.R: .
> test-constrain-dind.R: Bridging finished.
> test-constrain-dind.R:
> test-constrain-dind.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-dind.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-drop.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: 1
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-drop.R: Optimizing with step length 1.0000.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-drop.R: The log-likelihood improved by 0.0001.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R: Convergence test p-value: < 0.0001.
> test-drop.R: Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Fitting the dyad-independent submodel...
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Using 16 bridges:
> test-drop.R: 1
> test-drop.R: 2
> test-drop.R: 3
> test-drop.R: 4
> test-drop.R: 5
> test-drop.R: 6
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-drop.R: 7
> test-constraints.R: All terms are either offsets or extreme values. No optimization is performed.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Setting up bridge sampling...
> test-drop.R: 8
> test-drop.R: 9
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-drop.R: 10
> test-constraints.R: Using 16 bridges:
> test-constraints.R: 1
> test-drop.R: 11
> test-drop.R: 12
> test-drop.R: 13
> test-constraints.R: 2
> test-drop.R: 14
> test-drop.R: 15
> test-drop.R: 16
> test-constraints.R: 3
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constraints.R: 4
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-constraints.R: 5
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: 6
> test-constraints.R: 7
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-constraints.R: 8
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-constraints.R: 9
> test-constraints.R: 10
> test-constraints.R: 11
> test-drop.R: 1
> test-drop.R: Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by 0.0068.
> test-drop.R: Convergence test p-value: < 0.0001.
> test-drop.R: Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: 12
> test-drop.R: Fitting the dyad-independent submodel...
> test-constraints.R: 13
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-constraints.R: 14
> test-drop.R: Setting up bridge sampling...
> test-constraints.R: 15
> test-drop.R: Using 16 bridges:
> test-drop.R: 1
> test-drop.R: 2
> test-constraints.R: 16
> test-drop.R: 3
> test-drop.R: 4
> test-drop.R: 5
> test-drop.R: 6
> test-constraints.R: .
> test-drop.R: 7
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-drop.R: 8
> test-constraints.R:
> test-drop.R: 9
> test-drop.R: 10
> test-drop.R: 11
> test-drop.R: 12
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-drop.R: 13
> test-drop.R: 14
> test-constraints.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constraints.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constraints.R: Iteration 1 of at most 60:
> test-drop.R: 15
> test-constraints.R: Convergence test P-value:1.1e-05
> test-constraints.R: 1
> test-drop.R: 16
> test-constraints.R: The log-likelihood improved by 0.07919.
> test-constraints.R: Iteration 2 of at most 60:
> test-constraints.R: Convergence test P-value:3.7e-02
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-constraints.R: 1
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constraints.R: The log-likelihood improved by 0.01687.
> test-constraints.R: Iteration 3 of at most 60:
> test-constraints.R: Convergence test P-value:7e-01
> test-constraints.R: Convergence detected. Stopping.
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.0006029.
> test-constraints.R: Finished CD.
> test-constraints.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constraints.R: Iteration 1 of at most 60:
> test-drop.R: Evaluating network in model.
> test-drop.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-drop.R: 'ergm:MH_SPDyad'.
> test-drop.R: Initializing model...
> test-drop.R: Model initialized.
> test-drop.R: Using initial method 'MPLE'.
> test-drop.R: Initial parameters provided by caller: None.
> test-drop.R: number of free parameters: 7
> test-drop.R: number of fixed parameters: 0
> test-drop.R: Observed statistic(s) triangle and kstar5 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Fitting initial model.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Density guard set to 10000 from an initial count of 3 edges.
> test-drop.R:
> test-drop.R: Iteration 1 of at most 3 with parameter:
> test-drop.R: edges triangle degree2 kstar2 kstar5
> test-drop.R: 1.313175 -Inf 6.009173 -4.163221 -Inf
> test-drop.R: gwdegree gwdegree.decay
> test-drop.R: -2.860022 1.666671
> test-drop.R: Starting unconstrained MCMC...
> test-constraints.R: 1
> test-constraints.R: Optimizing with step length 1.0000.
> test-constraints.R: The log-likelihood improved by 0.0263.
> test-constraints.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constraints.R: Finished MCMLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Setting up bridge sampling...
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges:
> test-constraints.R: 1
> test-constraints.R: 2
> test-constraints.R: 3
> test-constraints.R: 4
> test-constraints.R: 5
> test-constraints.R: 6
> test-constraints.R: 7
> test-constraints.R: 8
> test-constraints.R: 9
> test-constraints.R: 10
> test-constraints.R: 11
> test-constraints.R: 12
> test-constraints.R: 13
> test-constraints.R: 14
> test-constraints.R: 15
> test-constraints.R: 16
> test-drop.R: Back from unconstrained MCMC.
> test-constraints.R: .
> test-drop.R: New interval = 512.
> test-drop.R: Average estimating function values:
> test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay
> test-drop.R: -0.29629630 -0.11934156 -0.11934156 -0.57005199 0.06446661
> test-drop.R: Starting MCMLE Optimization...
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: Using lognormal metric (see control.ergm function).
> test-drop.R: Optimizing loglikelihood
> test-drop.R: Starting MCMC s.e. computation.
> test-constraints.R:
> test-constraints.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constraints.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: The log-likelihood improved by 0.0385.
> test-drop.R: Estimated covariance matrix of the statistics has nullity 3. Effective parameter number adjusted to 2.
> test-drop.R: Test statistic: T^2 = 21.94136, with 2 free parameter(s) and 240.9959 degrees of freedom.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-drop.R: Fitting the dyad-independent submodel...
> test-drop.R: Dyad-independent submodel MLE has likelihood -11.02185 at:
> test-drop.R: [1] -2.639057 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
> test-drop.R: [8] 0.000000
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Initializing model and proposals...
> test-drop.R: Model and proposals initialized.
> test-drop.R: Using 16 bridges: Running theta=[-1.175967, -Inf, 6.177257,-4.435272, -Inf,-1.386658, 1.950625, 0.000000].
> test-drop.R: Running theta=[-1.270360, -Inf, 5.778724,-4.149126, -Inf,-1.297196, 1.824778, 0.000000].
> test-drop.R: Running theta=[-1.364753, -Inf, 5.380191,-3.862979, -Inf,-1.207735, 1.698931, 0.000000].
> test-drop.R: Running theta=[-1.459146, -Inf, 4.981659,-3.576833, -Inf,-1.118273, 1.573085, 0.000000].
> test-drop.R: Running theta=[-1.553539, -Inf, 4.583126,-3.290686, -Inf,-1.028811, 1.447238, 0.000000].
> test-drop.R: Running theta=[-1.6479316, -Inf, 4.1845933,-3.0045393, -Inf,-0.9393491, 1.3213911, 0.0000000].
> test-drop.R: Running theta=[-1.7423245, -Inf, 3.7860606,-2.7183927, -Inf,-0.8498873, 1.1955444, 0.0000000].
> test-drop.R: Running theta=[-1.8367174, -Inf, 3.3875279,-2.4322461, -Inf,-0.7604255, 1.0696976, 0.0000000].
> test-drop.R: Running theta=[-1.9311104, -Inf, 2.9889952,-2.1460995, -Inf,-0.6709636, 0.9438508, 0.0000000].
> test-drop.R: Running theta=[-2.0255033, -Inf, 2.5904625,-1.8599529, -Inf,-0.5815018, 0.8180040, 0.0000000].
> test-drop.R: Running theta=[-2.1198962, -Inf, 2.1919298,-1.5738063, -Inf,-0.4920400, 0.6921573, 0.0000000].
> test-drop.R: Running theta=[-2.2142891, -Inf, 1.7933971,-1.2876597, -Inf,-0.4025782, 0.5663105, 0.0000000].
> test-drop.R: Running theta=[-2.3086821, -Inf, 1.3948644,-1.0015131, -Inf,-0.3131164, 0.4404637, 0.0000000].
> test-drop.R: Running theta=[-2.4030750, -Inf, 0.9963317,-0.7153665, -Inf,-0.2236545, 0.3146169, 0.0000000].
> test-drop.R: Running theta=[-2.4974679, -Inf, 0.5977990,-0.4292199, -Inf,-0.1341927, 0.1887702, 0.0000000].
> test-drop.R: Running theta=[-2.59186086, -Inf, 0.19926635,-0.14307330, -Inf,-0.04473091, 0.06292339, 0.00000000].
> test-drop.R: .
> test-drop.R: Bridge sampling finished. Collating...
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'c
> test-ergm-proposal-unload.R: itation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'c
> test-ergm-proposal-unload.R: itation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (binary, valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ctriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ctriad (binary)
> test-ergm-term-doc.R: Cyclic triples
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: localtriangle(x) (binary)
> test-ergm-term-doc.R: Triangles within neighborhoods
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1) (binary)
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: opentriad (binary)
> test-ergm-term-doc.R: Open triads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: threetrail(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: threepath(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Three-trails
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: triangle(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: triangles(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangles
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: tripercent(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangle percentage
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ttriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ttriad (binary)
> test-ergm-term-doc.R: Transitive triples
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 31 matching ergm terms:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Definitions for term(s) b2factor :
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the
> test-ergm-term-doc.R: unique values of the attr attribute. Each of these statistics
> test-ergm-term-doc.R: gives the number of times a node with that attribute in the second mode of
> test-ergm-term-doc.R: the network appears in an edge. The second mode of a bipartite network
> test-ergm-term-doc.R: object is sometimes known as the "event" mode.
> test-ergm-term-doc.R: Keywords: bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3factor' were found. Try searching with search='b3factor'instead.
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Found 48 matching ergm terms:
> test-ergm-term-doc.R: B(formula, form) (valued)
> test-ergm-term-doc.R: Wrap binary terms for use in valued models
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Impose a curved structure on term parameters
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Exp(formula) (valued)
> test-ergm-term-doc.R: Exponentiate a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: For(...) (valued)
> test-ergm-term-doc.R: A for operator for terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Label(formula, label, pos) (valued)
> test-ergm-term-doc.R: Modify terms' coefficient names
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Log(formula, log0=-1/sqrt(.Machine$double.eps)) (valued)
> test-ergm-term-doc.R: Take a natural logarithm of a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Prod(formulas, label) (valued)
> test-ergm-term-doc.R: A product (or an arbitrary power combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: S(formula, attrs) (valued)
> test-ergm-term-doc.R: Evaluation on an induced subgraph
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Sum(formulas, label) (valued)
> test-ergm-term-doc.R: A sum (or an arbitrary linear combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiff(attr, pow=1, form="sum") (valued)
> test-ergm-term-doc.R: Absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiffcat(attr, base=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Categorical absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atleast(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values greater than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atmost(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values less than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: attrcov(attr, mat, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate by attribute pairing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalties(threshold=0) (valued)
> test-ergm-term-doc.R: Cyclical ties
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Cyclical weights
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edgecov(x, attrname=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edges (valued)
> test-ergm-term-doc.R: nonzero (valued)
> test-ergm-term-doc.R: Number of edges in the network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: equalto(value=0, tolerance=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values equal to a specific value (within tolerance)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: greaterthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly greater than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ininterval(lower=-Inf, upper=+Inf, open=c(TRUE,TRUE)) (valued)
> test-ergm-term-doc.R: Number of dyads whose values are in an interval
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mm(attrs, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Mixing matrix cells and margins
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mutual(form="min",threshold=0) (valued)
> test-ergm-term-doc.R: Mutuality
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecov(attr, form="sum") (valued)
> test-ergm-term-doc.R: nodemain(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of undirected dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodefactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of in-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeifactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: match(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Uniform homophily and differential homophily
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of out-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeofactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: receiver(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Receiver effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sender(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Sender effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: smallerthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly smaller than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Undirected degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sum(pow=1) (valued)
> test-ergm-term-doc.R: Sum of dyad values (optionally taken to a power)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: transitiveweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Transitive weights
> test-ergm-term-doc.R: Found 4 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DiscUnif(a,b)
> test-ergm-term-doc.R: Discrete Uniform reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: StdNormal
> test-ergm-term-doc.R: Standard Normal reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Unif(a,b)
> test-ergm-term-doc.R: Continuous Uniform reference
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 1 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R: Definitions for term(s) Bernoulli :
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference: Specifies each
> test-ergm-term-doc.R: dyad's baseline distribution to be Bernoulli with probability of
> test-ergm-term-doc.R: the tie being 0.5 . This is the only reference measure used
> test-ergm-term-doc.R: in binary mode.
> test-ergm-term-doc.R: Keywords: binary, discrete, finite, nonnegative
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'Cernoulli' were found. Try searching with search='Cernoulli'instead.
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrees
> test-ergm-term-doc.R: Preserve the receiver degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 0 matching ergm terms:
> test-ergm-term-doc.R: Found 16 matching ergm terms:
> test-ergm-term-doc.R: Dyads(fix=NULL, vary=NULL)
> test-ergm-term-doc.R: Constrain fixed or varying dyad-independent terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blockdiag(attr)
> test-ergm-term-doc.R: Block-diagonal structure constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blocks(attr=NULL, levels=NULL, levels2=FALSE, b1levels=NULL, b2levels=NULL)
> test-ergm-term-doc.R: Constrain blocks of dyads defined by mixing type on a vertex attribute.
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise(p01, p10)
> test-ergm-term-doc.R: A soft constraint to adjust the sampled distribution for
> test-ergm-term-doc.R: dyad-level noise with known perturbation probabilities
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: egocentric(attr=NULL, direction="both")
> test-ergm-term-doc.R: Preserve values of dyads incident on vertices with given attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixallbut(free.dyads)
> test-ergm-term-doc.R: Preserve the dyad status in all but the given edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixedas(fixed.dyads, present, absent)
> test-ergm-term-doc.R: Fix specific dyads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: hamming
> test-ergm-term-doc.R: Preserve the hamming distance to the given network (BROKEN: Do NOT Use)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: observed
> test-ergm-term-doc.R: Preserve the observed dyads of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Definitions for term(s) b1degrees :
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks: For bipartite networks, preserve the degree for the first mode of each vertex of the given
> test-ergm-term-doc.R: network, while allowing the degree for the second mode to vary.
> test-ergm-term-doc.R: Keywords: bipartite
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3degrees' were found. Try searching with search='b3degrees'instead.
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 2 matching ergm proposals:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R: Found 5 matching ergm proposals:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DistRLE
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Found 0 matching ergm proposals:
> test-ergm-term-doc.R: Found 18 matching ergm proposals:
> test-ergm-term-doc.R: BDStratTNT
> test-ergm-term-doc.R: TNT proposal with degree bounds, stratification, and a blocks constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegree
> test-ergm-term-doc.R: MHp for degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeDist
> test-ergm-term-doc.R: MHp for degreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeMix
> test-ergm-term-doc.R: MHp for degree mix constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegree
> test-ergm-term-doc.R: MHp for idegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegreeDist
> test-ergm-term-doc.R: MHp for idegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegree
> test-ergm-term-doc.R: MHp for odegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegreeDist
> test-ergm-term-doc.R: MHp for odegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingConstantEdges
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoiseTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Definitions for proposal(s) randomtoggle :
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Reference: Bernoulli Class: cross-sectional
> test-ergm-term-doc.R: May Enforce: .dyads bd
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No proposals named 'mandomtoggle' were found. Try searching with search='mandomtoggle'instead.
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-term-doc.R: * 'citation("ergm.count")' for citation information
> test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-term-doc.R:
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergmMPLE.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-ergmMPLE.R: Obtaining the responsible dyads.
> test-ergmMPLE.R: Evaluating the predictor and response matrix.
> test-ergmMPLE.R: Maximizing the pseudolikelihood.
> test-ergmMPLE.R: Finished MPLE.
> test-ergmMPLE.R: Evaluating log-likelihood at the estimate.
> test-ergmMPLE.R:
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R:
> test-ergm.bridge.llr.R: 9
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R:
> test-ergm.bridge.llr.R: 10
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-ergm.bridge.llr.R: 11
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gflomiss.R: 1
> test-gflomiss.R: Optimizing with step length 1.0000.
> test-gflomiss.R: The log-likelihood improved by 0.0010.
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: 2
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-gflomiss.R: Using 16 bridges:
> test-gflomiss.R: 1
> test-gflomiss.R: 2
> test-ergm.bridge.llr.R: 3
> test-gflomiss.R: 3
> test-gflomiss.R: 4
> test-gflomiss.R: 5
> test-gflomiss.R: 6
> test-ergm.bridge.llr.R: 4
> test-gflomiss.R: 7
> test-gflomiss.R: 8
> test-gflomiss.R: 9
> test-gflomiss.R: 10
> test-gflomiss.R: 11
> test-ergm.bridge.llr.R: 5
> test-gflomiss.R: 12
> test-gflomiss.R: 13
> test-gflomiss.R: 14
> test-gflomiss.R: 15
> test-gflomiss.R: 16
> test-ergm.bridge.llr.R: 6
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 7
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-gflomiss.R: 1
> test-gflomiss.R: Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 11
> test-gflomiss.R: The log-likelihood improved by 0.0003.
> test-gflomiss.R: Convergence test p-value: < 0.0001.
> test-gflomiss.R: Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: Using 16 bridges:
> test-gflomiss.R: 1
> test-gflomiss.R: 2
> test-gflomiss.R: 3
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: 4
> test-gflomiss.R: 5
> test-gflomiss.R: 6
> test-gflomiss.R: 7
> test-ergm.bridge.llr.R: 15
> test-gflomiss.R: 8
> test-gflomiss.R: 9
> test-gflomiss.R: 10
> test-gflomiss.R: 11
> test-gflomiss.R: 12
> test-gflomiss.R: 13
> test-gflomiss.R: 14
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: 15
> test-gflomiss.R: 16
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gmonkmiss.R: odegree3 odegree4 odegree5 odegree6
> test-gmonkmiss.R: 1 5 7 5
> test-gmonkmiss.R: idegree2 idegree3 idegree4 idegree5 idegree6 idegree7 idegree8 idegree10
> test-gmonkmiss.R: 3 5 1 3 2 1 1 1
> test-gmonkmiss.R: idegree11
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-ergm.bridge.llr.R: 2
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.6245.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-ergm.bridge.llr.R: 10
> test-gmonkmiss.R: Convergence test p-value: 0.0005.
> test-gmonkmiss.R: Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 11
> test-gmonkmiss.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-gmonkmiss.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-gmonkmiss.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-gmonkmiss.R: Iteration 1 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:3.3e-34
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 12
> test-gmonkmiss.R: The log-likelihood improved by 0.4205.
> test-gmonkmiss.R: Iteration 2 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.8e-14
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.1501.
> test-gmonkmiss.R: Iteration 3 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2e-04
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.03536.
> test-gmonkmiss.R: Iteration 4 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.6e-01
> test-ergm.bridge.llr.R: 13
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.007343.
> test-gmonkmiss.R: Iteration 5 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2.4e-01
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.00569.
> test-gmonkmiss.R: Iteration 6 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:9.9e-02
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.00904.
> test-gmonkmiss.R: Iteration 7 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:7.6e-01
> test-gmonkmiss.R: Convergence detected. Stopping.
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.001102.
> test-gmonkmiss.R: Finished CD.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.4514.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 9
> test-gmonkmiss.R: The log-likelihood improved by 0.0105.
> test-ergm.bridge.llr.R: 10
> test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 11
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-ergm.bridge.llr.R: 12
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-ergm.bridge.llr.R: 13
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.7035.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-gmonkmiss.R: Convergence test p-value: < 0.0001.
> test-gmonkmiss.R: Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-miss-dep.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-miss-dep.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss-dep.R: Iteration 1 of at most 60:
> test-miss-dep.R: Convergence test P-value:4.6e-47
> test-miss-dep.R: 1
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4613.
> test-miss-dep.R: The log-likelihood improved by 1.824.
> test-miss-dep.R: Iteration 2 of at most 60:
> test-metrics.R: The log-likelihood improved by 4.1429.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.5e-23
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.6054.
> test-miss-dep.R: Iteration 3 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.1e-07
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.1283.
> test-miss-dep.R: Iteration 4 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.8364.
> test-miss-dep.R: Convergence test P-value:3.3e-04
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.05435.
> test-metrics.R: The log-likelihood improved by 4.7215.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: Iteration 5 of at most 60:
> test-miss-dep.R: Convergence test P-value:2.1e-01
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.006185.
> test-miss-dep.R: Iteration 6 of at most 60:
> test-miss-dep.R: Convergence test P-value:4.1e-01
> test-miss-dep.R: 1
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-miss-dep.R: The log-likelihood improved by 0.002664.
> test-miss-dep.R: Iteration 7 of at most 60:
> test-metrics.R: The log-likelihood improved by 1.1346.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.6e-01
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.007694.
> test-miss-dep.R: Iteration 8 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.9e-01
> test-miss-dep.R: 1
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1129.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss-dep.R: The log-likelihood improved by 0.006878.
> test-miss-dep.R: Iteration 9 of at most 60:
> test-miss-dep.R: Convergence test P-value:7.8e-01
> test-miss-dep.R: Convergence detected. Stopping.
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.0003111.
> test-miss-dep.R: Finished CD.
> test-miss-dep.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0037.
> test-metrics.R: Convergence test p-value: 0.0004.
> test-metrics.R: Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: Post-burnin sample is constant; returning.
> test-miss-dep.R: 1
> test-miss-dep.R: Optimizing with step length 1.0000.
> test-miss-dep.R: The log-likelihood improved by 0.0017.
> test-miss-dep.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss-dep.R: Finished MCMLE.
> test-miss-dep.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4018.
> test-metrics.R: The log-likelihood improved by 3.2780.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss-dep.R: Using 16 bridges:
> test-miss-dep.R: 1
> test-miss-dep.R: 2
> test-miss-dep.R: 3
> test-miss-dep.R: 4
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.6020.
> test-metrics.R: The log-likelihood improved by 3.4584.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: 5
> test-miss-dep.R: 6
> test-miss-dep.R: 7
> test-miss-dep.R: 8
> test-miss-dep.R: 9
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.2132.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: 10
> test-miss-dep.R: 11
> test-miss-dep.R: 12
> test-miss-dep.R: 13
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-miss-dep.R: 14
> test-metrics.R: The log-likelihood improved by 0.0377.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss-dep.R: 15
> test-miss-dep.R: 16
> test-miss-dep.R: .
> test-miss-dep.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-miss-dep.R: model likelihood is only implemented for dyad-independent
> test-miss-dep.R: constraints
> test-miss-dep.R: at this time. Number of observations is similarly poorly defined. This
> test-miss-dep.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-miss-dep.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-miss-dep.R: distribution and constraints.
> test-miss-dep.R:
> test-miss-dep.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss-dep.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: n=
> test-miss.CD.R: 20, density=0.1, missing=0.1
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4158.
> test-metrics.R: The log-likelihood improved by 1.9115.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4804.
> test-metrics.R: The log-likelihood improved by 2.5519.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:3e-13
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.2096.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-12
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1974.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-miss.CD.R: Convergence test P-value:5.8e-11
> test-miss.CD.R: 1
> test-metrics.R: The log-likelihood improved by 2.9415.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 0.341.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4e-15
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1744.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-16
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6862.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.3e-15
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.3025.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-19
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1888.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.3e-17
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.2862.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-miss.CD.R: Convergence test P-value:1.6e-07
> test-miss.CD.R: 1
> test-metrics.R: The log-likelihood improved by 0.1226.
> test-miss.CD.R: The log-likelihood improved by 0.2007.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-metrics.R: Convergence test p-value: < 0.0001.
> test-metrics.R: Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:2.1e-02
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.03854.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-05
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1259.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.0004877.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.5e-68
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.7099.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2
> test-metrics.R: 3
> test-metrics.R: 4 5
> test-metrics.R: 6
> test-metrics.R: 7
> test-metrics.R: 8 9
> test-metrics.R: 10 11
> test-miss.CD.R: Convergence test P-value:1.6e-64
> test-metrics.R: Optimizing with step length 0.3934.
> test-miss.CD.R: 1
> test-metrics.R: The log-likelihood improved by 4.1457.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 0.5925.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.6e-53
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6327.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.1e-37
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.8025.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.8651.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-30
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.583.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-19
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6591.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.2e-04
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.05663.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.4e-01
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.007268.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.1e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.3360.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0827.
> test-metrics.R: Convergence test p-value: 0.0004.
> test-metrics.R: Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-54
> test-miss.CD.R: 1
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 0.5854.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.4e-52
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6164.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.5e-46
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6486.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-32
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 1.361.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.8e-14
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.379.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-03
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.05118.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.9e-02
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.03478.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.5e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.001186.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1
> test-metrics.R: 2 3 4
> test-metrics.R: 5
> test-metrics.R: 6
> test-metrics.R: 7 8
> test-metrics.R: 9 10
> test-metrics.R: 11
> test-metrics.R: Optimizing with step length 0.3701.
> test-metrics.R: The log-likelihood improved by 2.3554.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Network statistics:
> test-miss.CD.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.CD.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#11 esp#12
> test-miss.CD.R: esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.CD.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.CD.R: 0 0 0 0 0 0 0
> test-miss.CD.R: Correct estimate = -2.028148
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2
> test-metrics.R: 3
> test-metrics.R: 4
> test-metrics.R: 5
> test-metrics.R: 6 7
> test-metrics.R: 8
> test-metrics.R: 9
> test-metrics.R: 10
> test-metrics.R: 11
> test-metrics.R: 12
> test-metrics.R: Optimizing with step length 0.5218.
> test-miss.CD.R: Convergence test P-value:1.8e-283
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: The log-likelihood improved by 2.9696.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 1.712.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-235
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: 1
> test-metrics.R: 2
> test-metrics.R: 3
> test-metrics.R: Optimizing with step length 0.8048.
> test-metrics.R: The log-likelihood improved by 1.8226.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 1.493.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.3e-184
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.167.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.2705.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.9e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0012.
> test-metrics.R: Convergence test p-value: 0.0099. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-147
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.2e-155
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.8e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2
> test-metrics.R: 3 4
> test-metrics.R: 5 6
> test-metrics.R: 7
> test-metrics.R: 8
> test-metrics.R: 9
> test-metrics.R: 10 11
> test-metrics.R: 12
> test-metrics.R: 13 Optimizing with step length 0.4397.
> test-metrics.R: The log-likelihood improved by 3.0119.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-153
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2
> test-metrics.R: 3
> test-metrics.R: 4 5
> test-metrics.R: 6
> test-metrics.R: 7 8 9
> test-metrics.R: 10
> test-metrics.R: 11
> test-metrics.R: 12
> test-metrics.R: Optimizing with step length 0.6225.
> test-metrics.R: The log-likelihood improved by 3.6934.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:8e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.7e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 13 of at most 60:
> test-metrics.R: The log-likelihood improved by 1.0488.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-156
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 14 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-162
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0821.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 15 of at most 60:
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.3e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 16 of at most 60:
> test-miss.R: n=20, density=0.1, missing=0.05
> test-miss.R: Correct estimate = -2.118156 with log-likelihood -120.6883 .
> test-miss.CD.R: Convergence test P-value:1.1e-159
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 17 of at most 60:
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R:
> test-miss.R: MPLE estimate = -2.118156 with log-likelihood -120.6883 OK.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.CD.R: Convergence test P-value:3.3e-159
> test-miss.R: Using initial method 'MPLE'.
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 26 dyads is required.
> test-miss.R: Imputing 3 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 41 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 18 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.6e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 19 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.5e-163
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 20 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 49.45267
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4 5
> test-miss.R: 6 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: 10 11
> test-miss.R: 12
> test-miss.R: Optimizing with step length 0.4099.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.5936.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.374066
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:3.5e-156
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 21 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.36008
> test-miss.R: Starting MCMLE Optimization...
> test-miss.CD.R: Convergence test P-value:2.3e-153
> test-miss.R: 1
> test-miss.CD.R: 1
> test-miss.R: 2 3 4 5 6
> test-miss.CD.R: 2
> test-miss.R: 7 8 9
> test-miss.R: 10 11 12 Optimizing with step length 0.4981.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.2702.
> test-miss.R: Distance from origin on tolerance region scale: 192.073 (previously 422.077).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.647302
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 22 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1e-157
> test-miss.CD.R: 1 2
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 17.50766
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 23 of at most 60:
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: 2 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: Optimizing with step length 0.7736.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.0776.
> test-miss.R: Distance from origin on tolerance region scale: 71.38353 (previously 259.1767).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.95412
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.4e-160
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 5.621399
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.R: The log-likelihood improved by 0.3258.
> test-miss.R: Distance from origin on tolerance region scale: 6.488425 (previously 62.9371).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 5 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.070021
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Iteration 24 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.1e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 25 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 32.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 1.853909
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0589.
> test-miss.R: Distance from origin on tolerance region scale: 1.17581 (previously 10.81058).
> test-miss.R: Test statistic: T^2 = 11.54078, with 1 free parameter(s) and 179.1884 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0008. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.CD.R: Convergence test P-value:1.3e-150
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 26 of at most 60:
> test-miss.R: Dyad-independent submodel MLE has likelihood -120.6883 at:
> test-miss.R: [1] -2.118156 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.CD.R: Convergence test P-value:4.5e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Model and proposals initialized.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 27 of at most 60:
> test-miss.R: Initializing constrained model and proposals...
> test-miss.CD.R: Convergence test P-value:2.8e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges:
> test-miss.R: Running theta=[-2.133081, 0.000000].
> test-miss.R: Running theta=[-2.132118, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 28 of at most 60:
> test-miss.R: Running theta=[-2.131155, 0.000000].
> test-miss.R: Running theta=[-2.130192, 0.000000].
> test-miss.CD.R: Convergence test P-value:8.6e-160
> test-miss.R: Running theta=[-2.129229, 0.000000].
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.128267, 0.000000].
> test-miss.R: Running theta=[-2.127304, 0.000000].
> test-miss.R: Running theta=[-2.126341, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 29 of at most 60:
> test-miss.R: Running theta=[-2.125378, 0.000000].
> test-miss.R: Running theta=[-2.124415, 0.000000].
> test-miss.R: Running theta=[-2.123452, 0.000000].
> test-miss.R: Running theta=[-2.122489, 0.000000].
> test-miss.R: Running theta=[-2.121526, 0.000000].
> test-miss.CD.R: Convergence test P-value:2.9e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.120563, 0.000000].
> test-miss.R: Running theta=[-2.1196, 0.0000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 30 of at most 60:
> test-miss.R: Running theta=[-2.118637, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.009575496) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.119005, 0.000000].
> test-miss.R: Running theta=[-2.119968, 0.000000].
> test-miss.R: Running theta=[-2.120931, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.121894, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 31 of at most 60:
> test-miss.R: Running theta=[-2.122857, 0.000000].
> test-miss.R: Running theta=[-2.12382, 0.00000].
> test-miss.R: Running theta=[-2.124783, 0.000000].
> test-miss.R: Running theta=[-2.125746, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.9e-155
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.126709, 0.000000].
> test-miss.R: Running theta=[-2.127671, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 32 of at most 60:
> test-miss.R: Running theta=[-2.128634, 0.000000].
> test-miss.R: Running theta=[-2.129597, 0.000000].
> test-miss.R: Running theta=[-2.13056, 0.00000].
> test-miss.R: Running theta=[-2.131523, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.5e-156
> test-miss.R: Running theta=[-2.132486, 0.000000].
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.133449, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 33 of at most 60:
> test-miss.R: Estimated standard error (0.007542247) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132854, 0.000000].
> test-miss.R: Running theta=[-2.131891, 0.000000].
> test-miss.R: Running theta=[-2.130928, 0.000000].
> test-miss.R: Running theta=[-2.129965, 0.000000].
> test-miss.CD.R: Convergence test P-value:1e-153
> test-miss.CD.R: 1
> test-miss.R: Running theta=[-2.129002, 0.000000].
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.128039, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 34 of at most 60:
> test-miss.R: Running theta=[-2.127076, 0.000000].
> test-miss.R: Running theta=[-2.126113, 0.000000].
> test-miss.R: Running theta=[-2.125151, 0.000000].
> test-miss.R: Running theta=[-2.124188, 0.000000].
> test-miss.R: Running theta=[-2.123225, 0.000000].
> test-miss.CD.R: Convergence test P-value:2.5e-165
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.122262, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 35 of at most 60:
> test-miss.R: Running theta=[-2.121299, 0.000000].
> test-miss.R: Running theta=[-2.120336, 0.000000].
> test-miss.R: Running theta=[-2.119373, 0.000000].
> test-miss.R: Running theta=[-2.11841, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.006188692) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.118778, 0.000000].
> test-miss.CD.R: Convergence test P-value:3.7e-164
> test-miss.R: Running theta=[-2.119741, 0.000000].
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.120704, 0.000000].
> test-miss.R: Running theta=[-2.121667, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 36 of at most 60:
> test-miss.R: Running theta=[-2.12263, 0.00000].
> test-miss.R: Running theta=[-2.123593, 0.000000].
> test-miss.R: Running theta=[-2.124555, 0.000000].
> test-miss.R: Running theta=[-2.125518, 0.000000].
> test-miss.R: Running theta=[-2.126481, 0.000000].
> test-miss.CD.R: Convergence test P-value:3.5e-165
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.127444, 0.000000].
> test-miss.R: Running theta=[-2.128407, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 37 of at most 60:
> test-miss.R: Running theta=[-2.12937, 0.00000].
> test-miss.R: Running theta=[-2.130333, 0.000000].
> test-miss.R: Running theta=[-2.131296, 0.000000].
> test-miss.R: Running theta=[-2.132259, 0.000000].
> test-miss.CD.R: Convergence test P-value:3.8e-167
> test-miss.CD.R: 1
> test-miss.R: Running theta=[-2.133222, 0.000000].
> test-miss.CD.R: 2
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005396227) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132626, 0.000000].
> test-miss.R: Running theta=[-2.131664, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 38 of at most 60:
> test-miss.R: Running theta=[-2.130701, 0.000000].
> test-miss.R: Running theta=[-2.129738, 0.000000].
> test-miss.R: Running theta=[-2.128775, 0.000000].
> test-miss.R: Running theta=[-2.127812, 0.000000].
> test-miss.CD.R: Convergence test P-value:4.3e-166
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.126849, 0.000000].
> test-miss.R: Running theta=[-2.125886, 0.000000].
> test-miss.R: Running theta=[-2.124923, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 39 of at most 60:
> test-miss.R: Running theta=[-2.12396, 0.00000].
> test-miss.R: Running theta=[-2.122997, 0.000000].
> test-miss.R: Running theta=[-2.122035, 0.000000].
> test-miss.R: Running theta=[-2.121072, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.8e-166
> test-miss.R: Running theta=[-2.120109, 0.000000].
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.119146, 0.000000].
> test-miss.R: Running theta=[-2.118183, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 40 of at most 60:
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.4e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 41 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-139
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 42 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.6e-160
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1728:32768
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 486
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 1.8539 5.6566 0.2566 0.4463
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -8.881 -1.881 2.119 5.119 14.119
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -2.129e-17 1.663e+00 1.067e-01 1.067e-01
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.8807 -1.3807 0.1193 1.1193 4.0693
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R:
> test-miss.R: edges (Omni)
> test-miss.R: diff. 1.853909e+00 NA
> test-miss.R: test stat. 4.040502e+00 1.632566e+01
> test-miss.R: P-val. 5.333689e-05 7.904382e-05
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 0.50229634
> test-miss.R: Lag 128 0.27968825
> test-miss.R: Lag 192 0.14989734
> test-miss.R: Lag 256 0.08553620
> test-miss.R: Lag 320 0.01488518
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.04999730
> test-miss.R: Lag 128 0.05488745
> test-miss.R: Lag 192 -0.03080175
> test-miss.R: Lag 256 0.03734623
> test-miss.R: Lag 320 0.01289319
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.4658741
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.6413056
> test-miss.R: Joint P-value (lower = worse): 0.5503774
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain
> test-miss.R: 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.2553269
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.7984706
> test-miss.R: Joint P-value (lower = worse): 0.5503774 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -2.133563 with log-likelihood -120.7039 OK.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 43 of at most 60:
> test-miss.CD.R: Convergence test P-value:3e-152
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 44 of at most 60:
> test-miss.R: Correct estimate =
> test-miss.R: -1.663142 with log-likelihood -79.82064 .
> test-miss.CD.R: Convergence test P-value:3.1e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 45 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.9e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 46 of at most 60:
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R:
> test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.CD.R: Convergence test P-value:8.8e-148
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 8 dyads is required.
> test-miss.R: Imputing 1 edges at random.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 47 of at most 60:
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 30 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.8e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 48 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.8e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 49 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.15638
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6 7
> test-miss.CD.R: Convergence test P-value:6e-154
> test-miss.R: 8 Optimizing with step length 0.5368.
> test-miss.CD.R: 1 2
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 4.3000.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.146333
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 50 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 51 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 14.85185
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: 2
> test-miss.CD.R: Convergence test P-value:1.2e-150
> test-miss.R: 3 Optimizing with step length 1.0000.
> test-miss.CD.R: 1 2
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 3.2552.
> test-miss.R: Distance from origin on tolerance region scale: 64.83604 (previously 323.1391).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.584689
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 52 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.2e-161
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 53 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.600823
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1297.
> test-miss.R: Distance from origin on tolerance region scale: 2.582218 (previously 84.20395).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.684393
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:5.8e-145
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 54 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.2386831
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0011.
> test-miss.R: Distance from origin on tolerance region scale: 0.02175454 (previously 2.583022).
> test-miss.CD.R: Convergence test P-value:5.8e-163
> test-miss.R: Test statistic: T^2 = 16.95471, with 1 free parameter(s) and 192.1073 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.CD.R: 1 2
> test-miss.R: Initializing model to obtain the list of dyad-independent terms...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 55 of at most 60:
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Dyad-independent submodel MLE has likelihood -79.82064 at:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 56 of at most 60:
> test-miss.R: [1] -1.663142 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.CD.R: Convergence test P-value:2e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges:
> test-miss.R: Running theta=[-1.674863, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 57 of at most 60:
> test-miss.R: Running theta=[-1.674107, 0.000000].
> test-miss.R: Running theta=[-1.673351, 0.000000].
> test-miss.R: Running theta=[-1.672594, 0.000000].
> test-miss.R: Running theta=[-1.671838, 0.000000].
> test-miss.CD.R: Convergence test P-value:2.4e-151
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-1.671082, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 58 of at most 60:
> test-miss.R: Running theta=[-1.670326, 0.000000].
> test-miss.R: Running theta=[-1.66957, 0.00000].
> test-miss.R: Running theta=[-1.668813, 0.000000].
> test-miss.R: Running theta=[-1.668057, 0.000000].
> test-miss.R: Running theta=[-1.667301, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.2e-168
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-1.666545, 0.000000].
> test-miss.R: Running theta=[-1.665789, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 59 of at most 60:
> test-miss.R: Running theta=[-1.665033, 0.000000].
> test-miss.R: Running theta=[-1.664276, 0.000000].
> test-miss.R: Running theta=[-1.66352, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005483681) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-1.663809, 0.000000].
> test-miss.CD.R: Convergence test P-value:3e-159
> test-miss.R: Running theta=[-1.664565, 0.000000].
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-1.665321, 0.000000].
> test-miss.R: Running theta=[-1.666078, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 60 of at most 60:
> test-miss.R: Running theta=[-1.666834, 0.000000].
> test-miss.R: Running theta=[-1.66759, 0.00000].
> test-miss.R: Running theta=[-1.668346, 0.000000].
> test-miss.R: Running theta=[-1.669102, 0.000000].
> test-miss.R: Running theta=[-1.669858, 0.000000].
> test-miss.R: Running theta=[-1.670615, 0.000000].
> test-miss.R: Running theta=[-1.671371, 0.000000].
> test-miss.R: Running theta=[-1.672127, 0.000000].
> test-miss.R: Running theta=[-1.672883, 0.000000].
> test-miss.CD.R: Convergence test P-value:6.5e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-1.673639, 0.000000].
> test-miss.R: Running theta=[-1.674396, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Running theta=[-1.675152, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.2387 5.2156 0.3346 0.3867
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -9.2346 -4.2346 -0.2346 2.7654 11.7154
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 4.040e-17 1.007e+00 6.463e-02 6.463e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -1.2346 -0.7346 -0.2346 0.7654 2.7154
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.2386831 NA
> test-miss.R: test stat. -0.6087923 0.3706280
> test-miss.R: P-val. 0.5426621 0.5433813
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.000000000
> test-miss.R: Lag 128 0.141730657
> test-miss.R: Lag 256 -0.008044799
> test-miss.R: Lag 384 0.039503814
> test-miss.R: Lag 512 0.016265240
> test-miss.R: Lag 640 0.025525909
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.03837238
> test-miss.R: Lag 128 0.06102163
> test-miss.R: Lag 192 0.01817600
> test-miss.R: Lag 256 -0.07663989
> test-miss.R: Lag 320 -0.02107378
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain
> test-miss.R:
> test-miss.R: 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -1.387683
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.1652336
> test-miss.R: Joint P-value (lower = worse): 0.2706448
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain
> test-miss.R: 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.5702328
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.5685198
> test-miss.R: Joint P-value (lower = worse): 0.2706448 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -1.675241 with log-likelihood -79.81794 OK.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: Correct estimate = -3.157 with log-likelihood -8.355963 .
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R:
> test-miss.R: MPLE estimate = -3.157 with log-likelihood -8.355963 OK.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 2 dyads is required.
> test-miss.R: Imputing 0 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 2 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.942387
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.8416.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.729057
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 0.9012346
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1847.
> test-miss.R: Distance from origin on tolerance region scale: 3.678483 (previously 39.20962).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -3.138904
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.05349794
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0008.
> test-miss.R: Distance from origin on tolerance region scale: 0.01512904 (previously 4.293514).
> test-miss.R: Test statistic: T^2 = 22.84874, with 1 free parameter(s) and 256.603 degrees of freedom.
> test-miss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -8.355963 at:
> test-miss.R: [1] -3.157 0.000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges:
> test-miss.R: Running theta=[-3.11196, 0.00000].
> test-miss.R: Running theta=[-3.114866, 0.000000].
> test-miss.R: Running theta=[-3.117772, 0.000000].
> test-miss.R: Running theta=[-3.120678, 0.000000].
> test-miss.R: Running theta=[-3.123584, 0.000000].
> test-miss.R: Running theta=[-3.126489, 0.000000].
> test-miss.R: Running theta=[-3.129395, 0.000000].
> test-miss.R: Running theta=[-3.132301, 0.000000].
> test-miss.R: Running theta=[-3.135207, 0.000000].
> test-miss.R: Running theta=[-3.138113, 0.000000].
> test-miss.R: Running theta=[-3.141018, 0.000000].
> test-miss.R: Running theta=[-3.143924, 0.000000].
> test-miss.R: Running theta=[-3.14683, 0.00000].
> test-miss.R: Running theta=[-3.149736, 0.000000].
> test-miss.R: Running theta=[-3.152642, 0.000000].
> test-miss.R: Running theta=[-3.155548, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 3584:65536
> test-miss.R: Thinning interval = 256
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.05350 1.39509 0.08949 0.08949
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.05761 -1.05761 -0.05761 0.94239 2.94239
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 9.838e-19 2.335e-01 1.498e-02 1.763e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -0.05761 -0.05761 -0.05761 -0.05761 0.94239
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R:
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.05349794 NA
> test-miss.R: test stat. -0.58650248 0.3476007
> test-miss.R: P-val. 0.55753790 0.5559932
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0
> test-miss.R: 1.00000000
> test-miss.R: Lag 256 -0.07640761
> test-miss.R: Lag 512 0.04674441
> test-miss.R: Lag 768 -0.08703223
> test-miss.R: Lag 1024 -0.01273911
> test-miss.R: Lag 1280 0.08918131
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 128 0.01440843
> test-miss.R: Lag 256 -0.06163854
> test-miss.R: Lag 384 -0.05752332
> test-miss.R: Lag 512 0.09381586
> test-miss.R: Lag 640 0.16935966
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain
> test-miss.R: 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 1.222791
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.2214088
> test-miss.R: Joint P-value (lower = worse): 0.530107
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain
> test-miss.R: 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 0.899493
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.3683901
> test-miss.R: Joint P-value (lower = worse): 0.530107 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -3.110507 with log-likelihood -8.357166 OK.
> test-miss.R: Network statistics:
> test-miss.R: edges esp#1 esp#2 esp#3 esp#4
> test-miss.R: esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.R: 0 0 0 0 0 0 0
> test-miss.R: Correct estimate = -2.028148
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Iteration 1 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6
> test-miss.R: 7 8
> test-miss.R: 9
> test-miss.R: 10
> test-miss.R: 11
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 2 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 3 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3 4
> test-miss.R: 5 6
> test-miss.R: 7 8 9
> test-miss.R: 10
> test-miss.R: 11 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 4 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6
> test-miss.R: 7
> test-miss.R: 8
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 5 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
> test-miss.R: MCMLE estimation did not converge after 5 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Using 16 bridges:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6
> test-miss.R: 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: 10
> test-miss.R: 11
> test-miss.R: 12
> test-miss.R: 13
> test-miss.R: 14
> test-miss.R: 15
> test-miss.R: 16
> test-miss.R: .
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-largenetwork.R:
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-largenetwork.R:
> test-mple-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-offset.R: Obtaining the responsible dyads.
> test-mple-offset.R: Evaluating the predictor and response matrix.
> test-mple-offset.R: Maximizing the pseudolikelihood.
> test-mple-offset.R: Finished MPLE.
> test-mple-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-offset.R: Iteration 1 of at most 60:
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-mple-offset.R: 1
> test-mple-offset.R: Optimizing with step length 1.0000.
> test-mple-offset.R: The log-likelihood improved by 0.0040.
> test-mple-offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-mple-offset.R: Finished MCMLE.
> test-mple-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-mple-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-target.R: [1] 350 50 250
> test-mple-target.R: Structural check:
> test-mple-target.R: Mean degree: 1.4 .
> test-mple-target.R: Average degree among nodes with degree 2 or higher: 2.25 .
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-target.R: Iteration 1 of at most 60:
> test-networkLite.R: Loading required package: networkLite
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Estimating Bootstrap Standard Errors using 500 simulated networks.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10 11
> test-networkLite.R: 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7 8
> test-networkLite.R: 9 10 11
> test-networkLite.R: 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3 4 5 6 7
> test-networkLite.R: 8 9
> test-networkLite.R: 10 11
> test-networkLite.R: 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4 5
> test-networkLite.R: 6
> test-networkLite.R: 7 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-networkLite.R: Convergence test p-value: 0.0019.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-networkLite.R: Convergence test p-value: 0.0019.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247.
> test-networkLite.R: Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-networkLite.R: Convergence test p-value: 0.0085.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12
> test-networkLite.R: 13
> test-networkLite.R: 14
> test-networkLite.R: 15 16
> test-networkLite.R: 17
> test-networkLite.R: 18
> test-networkLite.R: 19
> test-networkLite.R: 20
> test-networkLite.R: 21
> test-networkLite.R: 22
> test-networkLite.R: 23
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9 10
> test-networkLite.R: 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247.
> test-networkLite.R: Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-networkLite.R: 1
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nodrop.R: Finished MPLE.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 1.0000.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-nodrop.R: The log-likelihood improved by 0.0005.
> test-nodrop.R: Convergence test p-value: < 0.0001.
> test-nodrop.R: Converged with 99% confidence.
> test-nodrop.R: Finished MCMLE.
> test-networkLite.R: Finished MPLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.2247.
> test-nodrop.R: The log-likelihood improved by 2.2822.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12
> test-networkLite.R: 13
> test-networkLite.R: 14
> test-networkLite.R: 15
> test-networkLite.R: 16
> test-networkLite.R: 17
> test-networkLite.R: 18
> test-networkLite.R: 19 20
> test-networkLite.R: 21
> test-networkLite.R: 22
> test-nodrop.R: 1
> test-networkLite.R: 23
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nodrop.R: Optimizing with step length 0.2817.
> test-nodrop.R: The log-likelihood improved by 2.4734.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 4 5
> test-networkLite.R: 6 7 8
> test-networkLite.R: 9 10
> test-networkLite.R: 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.2675.
> test-nodrop.R: The log-likelihood improved by 3.3752.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.3098.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-nodrop.R: The log-likelihood improved by 2.4783.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-nonident-test.R: 1
> test-nonident-test.R: Optimizing with step length 1.0000.
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-nonident-test.R: 1 Optimizing with step length 0.8147.
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: Convergence test p-value: 0.0031.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-nonunique-names.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonunique-names.R: Obtaining the responsible dyads.
> test-nonunique-names.R: Evaluating the predictor and response matrix.
> test-nonunique-names.R: Maximizing the pseudolikelihood.
> test-nonunique-names.R: Finished MPLE.
> test-nonunique-names.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonunique-names.R: Iteration 1 of at most 1:
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-nonunique-names.R: 1 Optimizing with step length 1.0000.
> test-nonunique-names.R: The log-likelihood improved by 0.0084.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-nonunique-names.R: Convergence test p-value: 0.0480. Not converged with 99% confidence; increasing sample size.
> test-nonunique-names.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonunique-names.R: Finished MCMLE.
> test-nonunique-names.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonunique-names.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-nonunique-names.R: Sample statistics summary:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-nonunique-names.R:
> test-nonunique-names.R: Iterations = 2304:44032
> test-nonunique-names.R: Thinning interval = 128
> test-nonunique-names.R: Number of chains = 1
> test-nonunique-names.R: Sample size per chain = 327
> test-nonunique-names.R:
> test-nonunique-names.R: 1. Empirical mean and standard deviation for each variable,
> test-nonunique-names.R: plus standard error of the mean:
> test-nonunique-names.R:
> test-nonunique-names.R: Mean SD Naive SE Time-series SE
> test-nonunique-names.R: edgecov.a -0.2171 3.380 0.1869 0.3114
> test-nonunique-names.R: edgecov.a 0.1346 3.565 0.1971 0.4311
> test-nonunique-names.R:
> test-nonunique-names.R: 2. Quantiles for each variable:
> test-nonunique-names.R:
> test-nonunique-names.R: 2.5% 25% 50% 75% 97.5%
> test-nonunique-names.R: edgecov.a -7 -2 0 2 6.85
> test-nonunique-names.R: edgecov.a -7 -2 0 3 7.00
> test-nonunique-names.R:
> test-nonunique-names.R:
> test-nonunique-names.R: Are sample statistics significantly different from observed?
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-nonunique-names.R: edgecov.a edgecov.a (Omni)
> test-nonunique-names.R: diff. -0.2171254 0.1345566 NA
> test-nonunique-names.R: test stat. -0.6971750 0.3120903 1.2954084
> test-nonunique-names.R: P-val. 0.4856933 0.7549719 0.5307488
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics cross-correlations:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: edgecov.a 1.0000000 0.6853513
> test-nonunique-names.R: edgecov.a 0.6853513 1.0000000
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics auto-correlation:
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: Lag 0 1.000000000 1.00000000
> test-nonunique-names.R: Lag 128 0.592467538 0.65334235
> test-nonunique-names.R: Lag 256 0.298337375 0.41906307
> test-nonunique-names.R: Lag 384 0.082532656 0.22830518
> test-nonunique-names.R: Lag 512 -0.003477022 0.08811653
> test-nonunique-names.R: Lag 640 -0.090771181 0.03701357
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics burn-in diagnostic (Geweke):
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R:
> test-nonunique-names.R: Fraction in 1st window = 0.1
> test-nonunique-names.R: Fraction in 2nd window = 0.5
> test-nonunique-names.R:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: -0.41748721 0.04619135
> test-nonunique-names.R:
> test-nonunique-names.R: Individual P-values (lower = worse):
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: 0.6763221 0.9631577
> test-nonunique-names.R: Joint P-value (lower = worse): 0.8447246
> test-nonunique-names.R:
> test-nonunique-names.R: Note: MCMC diagnostics shown here are from the last round of
> test-nonunique-names.R: simulation, prior to computation of final parameter estimates.
> test-nonunique-names.R: Because the final estimates are refinements of those used for this
> test-nonunique-names.R: simulation run, these diagnostics may understate model performance.
> test-nonunique-names.R: To directly assess the performance of the final model on in-model
> test-nonunique-names.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-nonunique-names.R: GOF=~model).
> test-nonunique-names.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.6004.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.0061.
> test-offsets.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-offsets.R: Using 16 bridges: 1
> test-offsets.R: 2
> test-offsets.R: 3
> test-offsets.R: 4
> test-offsets.R: 5
> test-offsets.R: 6
> test-offsets.R: 7
> test-offsets.R: 8
> test-offsets.R: 9
> test-offsets.R: 10
> test-offsets.R: 11
> test-offsets.R: 12
> test-offsets.R: 13
> test-offsets.R: 14
> test-offsets.R: 15
> test-offsets.R: 16
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Evaluating log-likelihood at the estimate.
> test-operators.R:
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Evaluating log-likelihood at the estimate.
> test-operators.R:
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Evaluating log-likelihood at the estimate.
> test-operators.R:
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Evaluating log-likelihood at the estimate.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Evaluating log-likelihood at the estimate.
> test-operators.R:
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Evaluating log-likelihood at the estimate.
> test-operators.R:
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Evaluating log-likelihood at the estimate.
> test-operators.R:
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Evaluating log-likelihood at the estimate.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Evaluating log-likelihood at the estimate.
> test-operators.R:
> test-parallel.R:
> test-parallel.R: Attaching package: 'statnet.common'
> test-parallel.R:
> test-parallel.R: The following objects are masked from 'package:base':
> test-parallel.R:
> test-parallel.R: attr, order, replace
> test-parallel.R:
> test-parallel.R: parallel test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run.
> test-parallel.R: Skipping OpenMP test. This package installation was built without OpenMP support.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Evaluating log-likelihood at the estimate.
> test-predict.ergm.R:
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Evaluating log-likelihood at the estimate.
> test-predict.ergm.R:
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Evaluating log-likelihood at the estimate.
> test-predict.ergm.R:
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Evaluating log-likelihood at the estimate.
> test-predict.ergm.R:
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Evaluating log-likelihood at the estimate.
> test-predict.ergm.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.7959.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.0207.
> test-offsets.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-offsets.R: Using 16 bridges: 1
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 2
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 3
> test-offsets.R: 4
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 5
> test-offsets.R: 6
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 7
> test-offsets.R: 8
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 9
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 10
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 11
> test-offsets.R: 12
> test-offsets.R: 13
> test-offsets.R: 14
> test-offsets.R: 15
> test-offsets.R: 16
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-runtime-diags.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-runtime-diags.R: Obtaining the responsible dyads.
> test-runtime-diags.R: Evaluating the predictor and response matrix.
> test-runtime-diags.R: Maximizing the pseudolikelihood.
> test-runtime-diags.R: Finished MPLE.
> test-runtime-diags.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-runtime-diags.R: Iteration 1 of at most 60:
> test-runtime-diags.R: 1
> test-runtime-diags.R: Optimizing with step length 1.0000.
> test-runtime-diags.R: The log-likelihood improved by 0.0414.
> test-runtime-diags.R: Convergence test p-value: 0.0016.
> test-runtime-diags.R: Converged with 99% confidence.
> test-runtime-diags.R: Finished MCMLE.
> test-runtime-diags.R: This model was fit using MCMC. To examine model diagnostics and check
> test-runtime-diags.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-scoping.R: 1
> test-scoping.R: Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate.
> test-scoping.R: Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges:
> test-scoping.R: 1
> test-scoping.R: 2
> test-scoping.R: 3
> test-scoping.R: 4
> test-scoping.R: 5
> test-scoping.R: 6
> test-scoping.R: 7
> test-scoping.R: 8
> test-scoping.R: 9
> test-scoping.R: 10
> test-scoping.R: 11
> test-scoping.R: 12
> test-scoping.R: 13
> test-scoping.R: 14
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: 15
> test-scoping.R: 16
> test-scoping.R: .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-scoping.R: 1
> test-scoping.R: Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate.
> test-scoping.R: Fitting the dyad-independent submodel...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges:
> test-scoping.R: 1
> test-scoping.R: 2
> test-scoping.R: 3
> test-scoping.R: 4
> test-scoping.R: 5
> test-scoping.R: 6
> test-scoping.R: 7
> test-scoping.R: 8
> test-scoping.R: 9
> test-scoping.R: 10
> test-scoping.R: 11
> test-scoping.R: 12
> test-scoping.R: 13
> test-scoping.R: 14
> test-scoping.R: 15
> test-scoping.R: 16
> test-scoping.R: .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-shrink-into-CH.R: 1
> test-shrink-into-CH.R: 2
> test-shrink-into-CH.R: 3
> test-shrink-into-CH.R: 4
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-shrink-into-CH.R: 5 6
> test-shrink-into-CH.R: 7
> test-shrink-into-CH.R: 8
> test-shrink-into-CH.R: 9
> test-shrink-into-CH.R: 10
> test-shrink-into-CH.R: 11
> test-shrink-into-CH.R: 12
> test-shrink-into-CH.R: 13
> test-shrink-into-CH.R: 14
> test-shrink-into-CH.R: 15
> test-shrink-into-CH.R: 16
> test-shrink-into-CH.R: 17
> test-shrink-into-CH.R: 18
> test-shrink-into-CH.R: 19
> test-shrink-into-CH.R: 20
> test-shrink-into-CH.R: 1
> test-shrink-into-CH.R: 2
> test-shrink-into-CH.R: 3
> test-shrink-into-CH.R: 4
> test-shrink-into-CH.R: 5
> test-shrink-into-CH.R: 6
> test-shrink-into-CH.R: 7
> test-shrink-into-CH.R: 8
> test-shrink-into-CH.R: 9
> test-shrink-into-CH.R: 10
> test-shrink-into-CH.R: 11
> test-shrink-into-CH.R: 12
> test-shrink-into-CH.R: 13
> test-shrink-into-CH.R: 14
> test-shrink-into-CH.R: 15
> test-shrink-into-CH.R: 16
> test-shrink-into-CH.R: 17
> test-shrink-into-CH.R: 18
> test-shrink-into-CH.R: 19
> test-shrink-into-CH.R: 20
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-skip.R: Iteration 1 of at most 60:
> test-skip.R: 1
> test-skip.R: Optimizing with step length 1.0000.
> test-skip.R: The log-likelihood improved by 0.0360.
> test-skip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-skip.R: Finished MCMLE.
> test-skip.R: This model was fit using MCMC. To examine model diagnostics and check
> test-skip.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-snctrl.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-snctrl.R: Obtaining the responsible dyads.
> test-snctrl.R: Evaluating the predictor and response matrix.
> test-snctrl.R: Maximizing the pseudolikelihood.
> test-snctrl.R: Finished MPLE.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.7009355 0.2208488
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.6617183 0.1405334
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0034.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.5333754 -0.1317716 0.6729982
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.57231795 -0.05712682 0.44962020
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0007.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.1e-111
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 1.945.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-44
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.5962.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:4e-07
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.06364.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.6e-05
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.04097.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.3e-03
> test-stocapprox.R: 1
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: The log-likelihood improved by 0.01842.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.002083.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.743217 0.112619
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.7631980 0.1383531
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-98
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 1.862.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.5e-30
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.3427.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:3e-09
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.08204.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.9e-02
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.01313.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.2e-02
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.009411.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:2.6e-01
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.005336.
> test-stocapprox.R: Iteration 7 of at most 60:
> test-stocapprox.R: Convergence test P-value:7.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.0009177.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0022.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R:
> test-stocapprox.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-stocapprox.R: * 'news(package="ergm.count")' for changes since last version
> test-stocapprox.R: * 'citation("ergm.count")' for citation information
> test-stocapprox.R: * 'https://statnet.org' for help, support, and other information
> test-stocapprox.R:
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 60:
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 1.0000.
> test-target-offset.R: The log-likelihood improved by 0.0210.
> test-target-offset.R: Convergence test p-value: 0.0002.
> test-target-offset.R: Converged with 99% confidence.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Using 16 bridges:
> test-target-offset.R: 1
> test-target-offset.R: 2
> test-target-offset.R: 3
> test-target-offset.R: 4
> test-target-offset.R: 5
> test-target-offset.R: 6
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 7
> test-target-offset.R: 8
> test-target-offset.R: 9
> test-target-offset.R: 10
> test-target-offset.R: 11
> test-target-offset.R: 12
> test-target-offset.R: 13
> test-target-offset.R: 14
> test-target-offset.R: 15
> test-target-offset.R: 16
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 14336:262144
> test-target-offset.R: Thinning interval = 1024
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 0.55556 4.490 0.2880 0.2880
> test-target-offset.R: degree1 0.04527 2.005 0.1286 0.1286
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges -7 -2.5 0 3 9.00
> test-target-offset.R: degree1 -3 -1.0 0 1 4.95
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Are sample statistics significantly different from observed?
> test-target-offset.R: edges degree1 (Omni)
> test-target-offset.R: diff. 0.55555556 0.04526749 NA
> test-target-offset.R: test stat. 1.92893422 0.35200754 9.455405747
> test-target-offset.R: P-val. 0.05373903 0.72483261 0.009922641
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges degree1
> test-target-offset.R: edges 1.0000000 -0.7250142
> test-target-offset.R: degree1 -0.7250142 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges degree1
> test-target-offset.R: Lag 0 1.000000000 1.000000000
> test-target-offset.R: Lag 1024 -0.061427219
> test-target-offset.R: 0.030194492
> test-target-offset.R: Lag 2048 0.007868535 0.092075103
> test-target-offset.R: Lag 3072 0.018624816 0.047810603
> test-target-offset.R: Lag 4096 -0.047471894 0.007659206
> test-target-offset.R: Lag 5120 -0.073593205 -0.009870131
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Chain
> test-target-offset.R: 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.4498910 -0.1601093
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.652789 0.872795
> test-target-offset.R: Joint P-value (lower = worse): 0.8380775
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-target-offset.R: Obtaining the responsible dyads.
> test-target-offset.R: Evaluating the predictor and response matrix.
> test-target-offset.R: Maximizing the pseudolikelihood.
> test-target-offset.R: Finished MPLE.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 3:
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.7297.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 2 of at most 3:
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.6389.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 3 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1 Optimizing with step length 0.8386.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: MCMLE estimation did not converge after 3 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Using 16 bridges: 1
> test-target-offset.R: 2
> test-target-offset.R: 3
> test-target-offset.R: 4
> test-target-offset.R: 5
> test-target-offset.R: 6
> test-target-offset.R: 7
> test-target-offset.R: 8
> test-target-offset.R: 9
> test-target-offset.R: 10
> test-target-offset.R: 11
> test-target-offset.R: 12
> test-target-offset.R: 13
> test-target-offset.R: 14
> test-target-offset.R: 15
> test-target-offset.R: 16
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 3584:65536
> test-target-offset.R: Thinning interval = 256
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 12.77778 5.118432 0.32835 0.3283476
> test-target-offset.R: gwdegree 0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R: gwdegree.decay 0.01236 0.002961 0.00019 0.0001293
> test-target-offset.R: degree0 -0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges 3.000e+00 9.00000 13.00000 16.00000 23.00000
> test-target-offset.R: gwdegree 1.063e-12 1.00000 1.00000 1.00000 1.00000
> test-target-offset.R: gwdegree.decay 5.955e-03 0.01191 0.01191 0.01489 0.01489
> test-target-offset.R: degree0 -1.000e+00 -1.00000 -1.00000 -1.00000 0.00000
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: edges 1.0000000 0.2709648 0.6049364 -0.2709648
> test-target-offset.R: gwdegree 0.2709648 1.0000000 0.5143643 -1.0000000
> test-target-offset.R: gwdegree.decay 0.6049364 0.5143643 1.0000000 -0.5143643
> test-target-offset.R: degree0 -0.2709648 -1.0000000 -0.5143643 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: Lag 0 1.000000000 1.00000000 1.000000000 1.00000000
> test-target-offset.R: Lag 256 0.006056003 0.02865300 -0.034893817 0.02865300
> test-target-offset.R: Lag 512 0.062813023 -0.07862186 0.002608612 -0.07862186
> test-target-offset.R: Lag 768 -0.089332866 -0.07930006 -0.150790861 -0.07930006
> test-target-offset.R: Lag 1024 -0.091496281 -0.07564135 -0.189123113 -0.07564135
> test-target-offset.R: Lag 1280 0.030192390 0.03461402 0.073975025 0.03461402
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Chain 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.05663036 -1.34419649 0.38772965 1.34419649
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.9548397 0.1788849 0.6982161 0.1788849
> test-target-offset.R: Joint P-value (lower = worse): 0.3850888
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-term-Offset.R: 1
> test-term-Offset.R: Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0066.
> test-term-Offset.R: Convergence test p-value: < 0.0001.
> test-term-Offset.R: Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-term-Offset.R: 1
> test-term-Offset.R: Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0024.
> test-term-Offset.R: Convergence test p-value: < 0.0001.
> test-term-Offset.R: Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
Saving _problems/test-term-Offset-21.R
Saving _problems/test-term-Offset-25.R
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-b12nodematch.R: In term 'b1nodematch' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b1dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1factor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1twostar' in package 'ergm': Argument 'base' has been superseded by 'levels2', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: In term 'asymmetric' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter
> test-term-directed.R: . Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) ideg7+.homophily.group and ideg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b2dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) gwodeg.fixed.0 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: In term 'nodeifactor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latte
> test-term-directed.R: r. Note that its interpretation may be different.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R: Observed statistic(s) odeg7+ and odeg8+ are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R: Observed statistic(s) odeg6+.homophily.group, odeg7+.homophily.group, and odeg8+.homophily.group are at their smallest attainabl
> test-term-directed.R: e values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Observed statistic(s) edgecov.YearsTrusted are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-flexible.R: All terms are either offsets or extreme values. No optimization is performed.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R:
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-mm.R: Note: Term 'mm(~Grade >= 10, levels = -1)' skipped because it contributes no statistics.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R:
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R:
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-options.R: 1
> test-term-options.R: Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0013.
> test-term-options.R: Convergence test p-value: < 0.0001.
> test-term-options.R: Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Fitting the dyad-independent submodel...
> test-term-options.R: Bridging between the dyad-independent submodel and the full model...
> test-term-options.R: Setting up bridge sampling...
> test-term-options.R: Using 16 bridges:
> test-term-options.R: 1
> test-term-options.R: 2
> test-term-options.R: 3
> test-term-options.R: 4
> test-term-options.R: 5
> test-term-options.R: 6
> test-term-options.R: 7
> test-term-options.R: 8
> test-term-options.R: 9
> test-term-options.R: 10
> test-term-options.R: 11
> test-term-options.R: 12
> test-term-options.R: 13
> test-term-options.R: 14
> test-term-options.R: 15
> test-term-options.R: 16
> test-term-options.R: .
> test-term-options.R: Bridging finished.
> test-term-options.R:
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-options.R: 1
> test-term-options.R: Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0003.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'DiscUnif2' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-u-function.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'.
> test-valued-sim.R: mean=1, var=4, corr=0.3
> test-valued-sim.R: eta=(0.192307692307692,0.0824175824175824,0.362637362637363)
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated mean (stats only):0.9964445
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated means (target=1):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 1.0034478 0.9683903
> test-valued-sim.R: [2,] 1.0302079 NA 0.8923730
> test-valued-sim.R: [3,] 0.6870641 0.7628159 NA
> test-valued-sim.R: Simulated vars (target=4):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 3.863045 4.034701
> test-valued-sim.R: [2,] 3.944388 NA 3.888997
> test-valued-sim.R: [3,] 3.740890 4.202553 NA
> test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3):
> test-valued-sim.R: [1] 0.2781206 0.2324470 0.3420405
> test-valued-sim.R: ==== output='stats', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=
> test-valued-sim.R: 0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R:
> test-valued-sim.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-sim.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-sim.R: * 'citation("ergm.count")' for citation information
> test-valued-sim.R: * 'https://statnet.org' for help, support, and other information
> test-valued-sim.R:
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R:
> test-valued-terms.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-terms.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-terms.R: * 'citation("ergm.count")' for citation information
> test-valued-terms.R: * 'https://statnet.org' for help, support, and other information
> test-valued-terms.R:
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• empty test (1):
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-term-Offset.R:19:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `off <- ergm(nw ~ edges + offset(triangle), offset.coef = 0.1)` to throw a message.
── Failure ('test-term-Offset.R:23:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `Off <- ergm(nw ~ edges + Offset(~triangle, which = 1, coef = 0.1))` to throw a message.
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 4.10.1
Check: tests
Result: ERROR
Running ‘requireNamespaceTest.R’
Running ‘testthat.R’ [553s/506s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # File tests/testthat.R in package ergm, part of the Statnet suite of packages
> # for network analysis, https://statnet.org .
> #
> # This software is distributed under the GPL-3 license. It is free, open
> # source, and has the attribution requirements (GPL Section 7) at
> # https://statnet.org/attribution .
> #
> # Copyright 2003-2025 Statnet Commons
> ################################################################################
> library(testthat)
> library(ergm)
Loading required package: network
'network' 1.19.0 (2024-12-08), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4.10.1 (2025-08-26), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.
>
> test_check("ergm")
Starting 2 test processes.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges:
> test-basis.R: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges:
> test-basis.R: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: Attaching package: 'statnet.common'
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: The following objects are masked from 'package:base':
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: attr, order, replace
> test-bridge-target.stats.R:
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-bridge-target.stats.R:
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: Unable to match target stats. Using MCMLE estimation.
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-bridge-target.stats.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 1
> test-bridge-target.stats.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: The log-likelihood improved by 0.0219.
> test-bridge-target.stats.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-bridge-target.stats.R: Finished MCMLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges:
> test-bridge-target.stats.R: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 10
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: This model was fit using MCMC. To examine model diagnostics and check
> test-bridge-target.stats.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Setting up bridge sampling...
> test-bridge-target.stats.R: 2
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 3
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-bridge-target.stats.R: 4
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-bridge-target.stats.R: 5
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 6
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-bridge-target.stats.R: 7
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-bridge-target.stats.R: 8
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-bridge-target.stats.R: 9
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Setting up bridge sampling...
> test-bridge-target.stats.R: 4
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-bridge-target.stats.R: 5
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-bridge-target.stats.R: 6
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 7
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-bridge-target.stats.R: 8
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-bridge-target.stats.R: 9
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-bridge-target.stats.R: 10
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-bridge-target.stats.R: 3
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 4
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-bridge-target.stats.R: 5
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent
> test-basis.R: constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Iteration 1 of at most 60:
> test-checkpointing.R: Saving state in '/tmp/RtmpHxfkNF/working_dir/RtmpArxdjT/file2d87ed2535c2d9_001.RData'.
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-checkpointing.R: The log-likelihood improved by 0.0213.
> test-checkpointing.R: Step length converged once. Increasing MCMC sample size.
> test-checkpointing.R: Iteration 2 of at most 60:
> test-checkpointing.R: Saving state in '/tmp/RtmpHxfkNF/working_dir/RtmpArxdjT/file2d87ed2535c2d9_002.RData'.
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges:
> test-bridge-target.stats.R: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 5
> test-checkpointing.R: The log-likelihood improved by 0.0238.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: 6
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-bridge-target.stats.R: 7
> test-checkpointing.R: Using 16 bridges:
> test-checkpointing.R: 1
> test-checkpointing.R: 2
> test-checkpointing.R: 3
> test-checkpointing.R: 4
> test-checkpointing.R: 5
> test-bridge-target.stats.R: 8
> test-checkpointing.R: 6
> test-checkpointing.R: 7
> test-checkpointing.R: 8
> test-checkpointing.R: 9
> test-checkpointing.R: 10
> test-checkpointing.R: 11
> test-checkpointing.R: 12
> test-checkpointing.R: 13
> test-bridge-target.stats.R: 9
> test-checkpointing.R: 14
> test-checkpointing.R: 15
> test-checkpointing.R: 16
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 10
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Resuming from state saved in '/tmp/RtmpHxfkNF/working_dir/RtmpArxdjT/file2d87ed2535c2d9_002.RData'.
> test-checkpointing.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-checkpointing.R: The log-likelihood improved by 0.0145.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: 15
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-checkpointing.R: Using 16 bridges:
> test-checkpointing.R: 1
> test-checkpointing.R: 2
> test-checkpointing.R: 3
> test-bridge-target.stats.R: 16
> test-checkpointing.R: 4
> test-checkpointing.R: 5
> test-checkpointing.R: 6
> test-checkpointing.R: 7
> test-checkpointing.R: 8
> test-checkpointing.R: 9
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-checkpointing.R: 10
> test-checkpointing.R: 11
> test-checkpointing.R: 12
> test-checkpointing.R: 13
> test-checkpointing.R: 14
> test-checkpointing.R: 15
> test-checkpointing.R: 16
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constrain-degrees-edges.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constrain-degrees-edges.R: Iteration 1 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:1.6e-07
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.1482.
> test-constrain-degrees-edges.R: Iteration 2 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:2.7e-03
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.04365.
> test-constrain-degrees-edges.R: Finished CD.
> test-constrain-degrees-edges.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-degrees-edges.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondInDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R:
> test-constrain-blockdiag.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-constrain-blockdiag.R: * 'news(package="ergm.count")' for changes since last version
> test-constrain-blockdiag.R: * 'citation("ergm.count")' for citation information
> test-constrain-blockdiag.R: * 'https://statnet.org' for help, support, and other information
> test-constrain-blockdiag.R:
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constrain-dind.R: Iteration 1 of at most 60:
> test-constrain-dind.R: 1
> test-constrain-dind.R: Optimizing with step length 1.0000.
> test-constrain-dind.R: The log-likelihood improved by 0.0020.
> test-constrain-dind.R: Convergence test p-value: < 0.0001.
> test-constrain-dind.R: Converged with 99% confidence.
> test-constrain-dind.R: Finished MCMLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Fitting the dyad-independent submodel...
> test-constrain-dind.R: Bridging between the dyad-independent submodel and the full model...
> test-constrain-dind.R: Setting up bridge sampling...
> test-constrain-dind.R: Using 16 bridges: 1
> test-constrain-dind.R: 2
> test-constrain-dind.R: 3
> test-constrain-dind.R: 4
> test-constrain-dind.R: 5
> test-constrain-dind.R: 6
> test-constrain-dind.R: 7
> test-constrain-dind.R: 8
> test-constrain-dind.R: 9
> test-constrain-dind.R: 10
> test-constrain-dind.R: 11
> test-constrain-dind.R: 12
> test-constrain-dind.R: 13
> test-constrain-dind.R: 14
> test-constrain-dind.R: 15
> test-constrain-dind.R: 16
> test-constrain-dind.R: .
> test-constrain-dind.R: Bridging finished.
> test-constrain-dind.R:
> test-constrain-dind.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-dind.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constrain-dind.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constraints.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-constraints.R:
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-drop.R: 1
> test-drop.R: Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by 0.0001.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Fitting the dyad-independent submodel...
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Using 16 bridges: 1
> test-drop.R: 2
> test-drop.R: 3
> test-drop.R: 4
> test-drop.R: 5
> test-drop.R: 6
> test-drop.R: 7
> test-drop.R: 8
> test-drop.R: 9
> test-drop.R: 10
> test-drop.R: 11
> test-drop.R: 12
> test-drop.R: 13
> test-drop.R: 14
> test-drop.R: 15
> test-drop.R: 16
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-drop.R: 1
> test-drop.R: Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by 0.0068.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Fitting the dyad-independent submodel...
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Using 16 bridges: 1
> test-drop.R: 2
> test-drop.R: 3
> test-drop.R: 4
> test-drop.R: 5
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-drop.R: 6
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: 7
> test-constraints.R:
> test-drop.R: 8
> test-drop.R: 9
> test-drop.R: 10
> test-drop.R: 11
> test-drop.R: 12
> test-drop.R: 13
> test-drop.R: 14
> test-drop.R: 15
> test-drop.R: 16
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: Evaluating network in model.
> test-drop.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-drop.R: 'ergm:MH_SPDyad'.
> test-drop.R: Initializing model...
> test-drop.R: Model initialized.
> test-drop.R: Using initial method 'MPLE'.
> test-drop.R: Initial parameters provided by caller: None.
> test-drop.R: number of free parameters: 7
> test-drop.R: number of fixed parameters: 0
> test-drop.R: Observed statistic(s) triangle and kstar5 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Fitting initial model.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Density guard set to 10000 from an initial count of 3 edges.
> test-drop.R:
> test-drop.R: Iteration 1 of at most 3 with parameter:
> test-drop.R: edges triangle degree2 kstar2 kstar5
> test-drop.R: -5.244523e-01 -Inf 2.592553e-01 -3.147984e-01 -Inf
> test-drop.R: gwdegree gwdegree.decay
> test-drop.R: -9.254574e-01 5.033616e-11
> test-drop.R: Starting unconstrained MCMC...
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constraints.R: All terms are either offsets or extreme values. No optimization is performed.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Setting up bridge sampling...
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges:
> test-constraints.R: 1
> test-constraints.R: 2
> test-drop.R: Back from unconstrained MCMC.
> test-constraints.R: 3
> test-drop.R: New interval = 512.
> test-drop.R: Average estimating function values:
> test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay
> test-drop.R: 3.399177 1.395062 4.641975 3.370370 -2.170826
> test-drop.R: Starting MCMLE Optimization...
> test-drop.R: 1
> test-drop.R: Optimizing with step length 0.9524.
> test-drop.R: Using lognormal metric (see control.ergm function).
> test-drop.R: Optimizing loglikelihood
> test-drop.R: The log-likelihood improved by 1.5314.
> test-constraints.R: 4
> test-drop.R: Estimating equations are not within tolerance region.
> test-drop.R:
> test-drop.R: Iteration 2 of at most 3 with parameter:
> test-drop.R: edges triangle degree2 kstar2 kstar5
> test-drop.R: -7.679127e-01 -Inf 3.298966e-01 -4.787788e-01 -Inf
> test-drop.R: gwdegree gwdegree.decay
> test-drop.R: -1.324881e+00 7.047518e-09
> test-drop.R: Starting unconstrained MCMC...
> test-constraints.R: 5
> test-constraints.R: 6
> test-constraints.R: 7
> test-constraints.R: 8
> test-constraints.R: 9
> test-constraints.R: 10
> test-constraints.R: 11
> test-constraints.R: 12
> test-constraints.R: 13
> test-constraints.R: 14
> test-constraints.R: 15
> test-constraints.R: 16
> test-constraints.R: .
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-drop.R: Back from unconstrained MCMC.
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-drop.R: New interval = 256.
> test-drop.R: Average estimating function values:
> test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay
> test-drop.R: 0.2427984 -0.4115226 0.1934156 0.5020576 0.2889657
> test-drop.R: Starting MCMLE Optimization...
> test-drop.R: 1
> test-drop.R: Optimizing with step length 0.9524.
> test-drop.R: Using lognormal metric (see control.ergm function).
> test-drop.R: Optimizing loglikelihood
> test-constraints.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constraints.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constraints.R: Iteration 1 of at most 60:
> test-drop.R: The log-likelihood improved by 0.2272.
> test-drop.R: Distance from origin on tolerance region scale: 4.992214 (previously Inf).
> test-drop.R: Estimating equations are not within tolerance region.
> test-drop.R:
> test-drop.R: Iteration 3 of at most 3 with parameter:
> test-drop.R: edges triangle degree2 kstar2 kstar5
> test-drop.R: -1.0610699 -Inf 1.2599941 -0.5094538 -Inf
> test-drop.R: gwdegree gwdegree.decay
> test-drop.R: -1.4594425 0.1742153
> test-drop.R: Starting unconstrained MCMC...
> test-constraints.R: Convergence test P-value:1.1e-05
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.07919.
> test-constraints.R: Iteration 2 of at most 60:
> test-constraints.R: Convergence test P-value:3.7e-02
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.01687.
> test-constraints.R: Iteration 3 of at most 60:
> test-constraints.R: Convergence test P-value:7e-01
> test-constraints.R: Convergence detected. Stopping.
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.0006029.
> test-constraints.R: Finished CD.
> test-constraints.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constraints.R: Iteration 1 of at most 60:
> test-constraints.R: 1
> test-constraints.R: Optimizing with step length 1.0000.
> test-constraints.R: The log-likelihood improved by 0.0263.
> test-constraints.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constraints.R: Finished MCMLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Setting up bridge sampling...
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges: 1
> test-constraints.R: 2
> test-constraints.R: 3
> test-constraints.R: 4
> test-constraints.R: 5
> test-constraints.R: 6
> test-drop.R: Back from unconstrained MCMC.
> test-drop.R: New interval = 128.
> test-drop.R: Average estimating function values:
> test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay
> test-drop.R: 0.4855967 0.3621399 0.6090535 0.5209768 -0.5709305
> test-drop.R: Starting MCMLE Optimization...
> test-drop.R: 1
> test-drop.R: Optimizing with step length 0.9524.
> test-constraints.R: 7
> test-drop.R: Using lognormal metric (see control.ergm function).
> test-drop.R: Optimizing loglikelihood
> test-drop.R: Starting MCMC s.e. computation.
> test-constraints.R: 8
> test-drop.R: The log-likelihood improved by 0.0418.
> test-drop.R: Distance from origin on tolerance region scale: 0.9245623 (previously Inf).
> test-constraints.R: 9
> test-constraints.R: 10
> test-drop.R: Estimated covariance matrix of the statistics has nullity 1. Effective parameter number adjusted to 4.
> test-drop.R: Test statistic: T^2 = 10.64757, with 4 free parameter(s) and 238.9876 degrees of freedom.
> test-drop.R: Convergence test p-value: 0.0352.
> test-drop.R: Not converged with 99% confidence; increasing sample size.
> test-drop.R: 99% confidence critical value = 13.77129.
> test-drop.R: MCMLE estimation did not converge after 3 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-constraints.R: 11
> test-drop.R: Fitting the dyad-independent submodel...
> test-constraints.R: 12
> test-constraints.R: 13
> test-constraints.R: 14
> test-drop.R: Dyad-independent submodel MLE has likelihood -11.02185 at:
> test-drop.R: [1] -2.639057 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
> test-drop.R: [8] 0.000000
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Initializing model and proposals...
> test-constraints.R: 15
> test-constraints.R: 16
> test-constraints.R: .
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-constraints.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constraints.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: Model and proposals initialized.
> test-drop.R: Using 16 bridges:
> test-drop.R: Running theta=[-0.7985711, -Inf, 1.5423348,-0.6858812, -Inf,-1.5615909, 0.4556572, 0.0000000].
> test-drop.R: Running theta=[-0.9173121, -Inf, 1.4428293,-0.6416308, -Inf,-1.4608431, 0.4262599, 0.0000000].
> test-drop.R: Running theta=[-1.0360532, -Inf, 1.3433238,-0.5973804, -Inf,-1.3600953, 0.3968627, 0.0000000].
> test-drop.R: Running theta=[-1.1547942, -Inf, 1.2438184,-0.5531300, -Inf,-1.2593475, 0.3674655, 0.0000000].
> test-drop.R: Running theta=[-1.2735353, -Inf, 1.1443129,-0.5088796, -Inf,-1.1585997, 0.3380682, 0.0000000].
> test-drop.R: Running theta=[-1.3922763, -Inf, 1.0448074,-0.4646292, -Inf,-1.0578519, 0.3086710, 0.0000000].
> test-drop.R: Running theta=[-1.5110174, -Inf, 0.9453019,-0.4203788, -Inf,-0.9571041, 0.2792737, 0.0000000].
> test-drop.R: Running theta=[-1.6297584, -Inf, 0.8457965,-0.3761284, -Inf,-0.8563563, 0.2498765, 0.0000000].
> test-drop.R: Running theta=[-1.7484995, -Inf, 0.7462910,-0.3318780, -Inf,-0.7556085, 0.2204793, 0.0000000].
> test-drop.R: Running theta=[-1.8672405, -Inf, 0.6467855,-0.2876276, -Inf,-0.6548607, 0.1910820, 0.0000000].
> test-drop.R: Running theta=[-1.9859816, -Inf, 0.5472801,-0.2433772, -Inf,-0.5541129, 0.1616848, 0.0000000].
> test-drop.R: Running theta=[-2.1047226, -Inf, 0.4477746,-0.1991268, -Inf,-0.4533651, 0.1322876, 0.0000000].
> test-drop.R: Running theta=[-2.2234637, -Inf, 0.3482691,-0.1548764, -Inf,-0.3526173, 0.1028903, 0.0000000].
> test-drop.R: Running theta=[-2.34220471, -Inf, 0.24876367,-0.11062599, -Inf,-0.25186949, 0.07349309, 0.00000000].
> test-drop.R: Running theta=[-2.46094576, -Inf, 0.14925820,-0.06637560, -Inf,-0.15112170, 0.04409585, 0.00000000].
> test-drop.R: Running theta=[-2.57968680, -Inf, 0.04975273,-0.02212520, -Inf,-0.05037390, 0.01469862, 0.00000000].
> test-drop.R: .
> test-drop.R: Bridge sampling finished. Collating...
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-term-doc.R: * 'c
> test-ergm-term-doc.R: itation("ergm.count")' for citation information
> test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-term-doc.R:
> test-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 9 matching ergm terms:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (binary, valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ctriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ctriad (binary)
> test-ergm-term-doc.R: Cyclic triples
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: localtriangle(x) (binary)
> test-ergm-term-doc.R: Triangles within neighborhoods
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1) (binary)
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: opentriad (binary)
> test-ergm-term-doc.R: Open triads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: threetrail(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: threepath(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Three-trails
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: triangle(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: triangles(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangles
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: tripercent(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangle percentage
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ttriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ttriad (binary)
> test-ergm-term-doc.R: Transitive triples
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 31 matching ergm terms:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Definitions for term(s) b2factor :
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the
> test-ergm-term-doc.R: unique values of the attr attribute. Each of these statistics
> test-ergm-term-doc.R: gives the number of times a node with that attribute in the second mode of
> test-ergm-term-doc.R: the network appears in an edge. The second mode of a bipartite network
> test-ergm-term-doc.R: object is sometimes known as the "event" mode.
> test-ergm-term-doc.R: Keywords: bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3factor' were found. Try searching with search='b3factor'instead.
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 48 matching ergm terms:
> test-ergm-term-doc.R: B(formula, form) (valued)
> test-ergm-term-doc.R: Wrap binary terms for use in valued models
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Impose a curved structure on term parameters
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Exp(formula) (valued)
> test-ergm-term-doc.R: Exponentiate a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: For(...) (valued)
> test-ergm-term-doc.R: A for operator for terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Label(formula, label, pos) (valued)
> test-ergm-term-doc.R: Modify terms' coefficient names
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Log(formula, log0=-1/sqrt(.Machine$double.eps)) (valued)
> test-ergm-term-doc.R: Take a natural logarithm of a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Prod(formulas, label) (valued)
> test-ergm-term-doc.R: A product (or an arbitrary power combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: S(formula, attrs) (valued)
> test-ergm-term-doc.R: Evaluation on an induced subgraph
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Sum(formulas, label) (valued)
> test-ergm-term-doc.R: A sum (or an arbitrary linear combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiff(attr, pow=1, form="sum") (valued)
> test-ergm-term-doc.R: Absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiffcat(attr, base=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Categorical absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atleast(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values greater than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atmost(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values less than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: attrcov(attr, mat, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate by attribute pairing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalties(threshold=0) (valued)
> test-ergm-term-doc.R: Cyclical ties
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Cyclical weights
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edgecov(x, attrname=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edges (valued)
> test-ergm-term-doc.R: nonzero (valued)
> test-ergm-term-doc.R: Number of edges in the network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: equalto(value=0, tolerance=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values equal to a specific value (within tolerance)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: greaterthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly greater than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ininterval(lower=-Inf, upper=+Inf, open=c(TRUE,TRUE)) (valued)
> test-ergm-term-doc.R: Number of dyads whose values are in an interval
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mm(attrs, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Mixing matrix cells and margins
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mutual(form="min",threshold=0) (valued)
> test-ergm-term-doc.R: Mutuality
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecov(attr, form="sum") (valued)
> test-ergm-term-doc.R: nodemain(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of undirected dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodefactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of in-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeifactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: match(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Uniform homophily and differential homophily
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of out-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeofactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: receiver(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Receiver effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sender(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Sender effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: smallerthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly smaller than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Undirected degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sum(pow=1) (valued)
> test-ergm-term-doc.R: Sum of dyad values (optionally taken to a power)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: transitiveweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Transitive weights
> test-ergm-term-doc.R: Found 4 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DiscUnif(a,b)
> test-ergm-term-doc.R: Discrete Uniform reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: StdNormal
> test-ergm-term-doc.R: Standard Normal reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Unif(a,b)
> test-ergm-term-doc.R: Continuous Uniform reference
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 1 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R: Definitions for term(s) Bernoulli :
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference: Specifies each
> test-ergm-term-doc.R: dyad's baseline distribution to be Bernoulli with probability of
> test-ergm-term-doc.R: the tie being 0.5 . This is the only reference measure used
> test-ergm-term-doc.R: in binary mode.
> test-ergm-term-doc.R: Keywords: binary, discrete, finite, nonnegative
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'Cernoulli' were found. Try searching with search='Cernoulli'instead.
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 9 matching ergm terms:
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrees
> test-ergm-term-doc.R: Preserve the receiver degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 0 matching ergm terms:
> test-ergm-term-doc.R: Found 16 matching ergm terms:
> test-ergm-term-doc.R: Dyads(fix=NULL, vary=NULL)
> test-ergm-term-doc.R: Constrain fixed or varying dyad-independent terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blockdiag(attr)
> test-ergm-term-doc.R: Block-diagonal structure constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blocks(attr=NULL, levels=NULL, levels2=FALSE, b1levels=NULL, b2levels=NULL)
> test-ergm-term-doc.R: Constrain blocks of dyads defined by mixing type on a vertex attribute.
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise(p01, p10)
> test-ergm-term-doc.R: A soft constraint to adjust the sampled distribution for
> test-ergm-term-doc.R: dyad-level noise with known perturbation probabilities
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: egocentric(attr=NULL, direction="both")
> test-ergm-term-doc.R: Preserve values of dyads incident on vertices with given attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixallbut(free.dyads)
> test-ergm-term-doc.R: Preserve the dyad status in all but the given edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixedas(fixed.dyads, present, absent)
> test-ergm-term-doc.R: Fix specific dyads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: hamming
> test-ergm-term-doc.R: Preserve the hamming distance to the given network (BROKEN: Do NOT Use)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: observed
> test-ergm-term-doc.R: Preserve the observed dyads of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Definitions for term(s) b1degrees :
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks: For bipartite networks, preserve the degree for the first mode of each vertex of the given
> test-ergm-term-doc.R: network, while allowing the degree for the second mode to vary.
> test-ergm-term-doc.R: Keywords: bipartite
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3degrees' were found. Try searching with search='b3degrees'instead.
> test-ergm-term-doc.R: Found
> test-ergm-term-doc.R: 2 matching ergm proposals:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R: Found 5 matching ergm proposals:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DistRLE
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Found 0 matching ergm proposals:
> test-ergm-term-doc.R: Found 18 matching ergm proposals:
> test-ergm-term-doc.R: BDStratTNT
> test-ergm-term-doc.R: TNT proposal with degree bounds, stratification, and a blocks constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegree
> test-ergm-term-doc.R: MHp for degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeDist
> test-ergm-term-doc.R: MHp for degreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeMix
> test-ergm-term-doc.R: MHp for degree mix constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegree
> test-ergm-term-doc.R: MHp for idegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegreeDist
> test-ergm-term-doc.R: MHp for idegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegree
> test-ergm-term-doc.R: MHp for odegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegreeDist
> test-ergm-term-doc.R: MHp for odegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingConstantEdges
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoiseTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Definitions for proposal(s) randomtoggle :
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Reference: Bernoulli Class: cross-sectional
> test-ergm-term-doc.R: May Enforce: .dyads bd
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No proposals named 'mandomtoggle' were found. Try searching with search='mandomtoggle'instead.
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-term-doc.R: * 'citation("ergm.count")' for citation information
> test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-term-doc.R:
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergmMPLE.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-ergmMPLE.R: Obtaining the responsible dyads.
> test-ergmMPLE.R: Evaluating the predictor and response matrix.
> test-ergmMPLE.R: Maximizing the pseudolikelihood.
> test-ergmMPLE.R: Finished MPLE.
> test-ergmMPLE.R: Evaluating log-likelihood at the estimate.
> test-ergmMPLE.R:
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R:
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-ergm.bridge.llr.R: 2
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: 1
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 15
> test-gflomiss.R: The log-likelihood improved by < 0.0001.
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gflomiss.R: Using 16 bridges: 1
> test-gflomiss.R: 2
> test-gflomiss.R: 3
> test-gflomiss.R: 4
> test-gflomiss.R: 5
> test-ergm.bridge.llr.R: 2
> test-gflomiss.R: 6
> test-gflomiss.R: 7
> test-gflomiss.R: 8
> test-gflomiss.R: 9
> test-ergm.bridge.llr.R: 3
> test-gflomiss.R: 10
> test-gflomiss.R: 11
> test-gflomiss.R: 12
> test-gflomiss.R: 13
> test-ergm.bridge.llr.R: 4
> test-gflomiss.R: 14
> test-gflomiss.R: 15
> test-gflomiss.R: 16
> test-ergm.bridge.llr.R: 5
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-gflomiss.R: 1 Optimizing with step length 1.0000.
> test-gflomiss.R: The log-likelihood improved by 0.0113.
> test-ergm.bridge.llr.R: 11
> test-gflomiss.R: Convergence test p-value: 0.0006. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-gflomiss.R: Using 16 bridges: 1
> test-gflomiss.R: 2
> test-gflomiss.R: 3
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: 4
> test-gflomiss.R: 5
> test-gflomiss.R: 6
> test-gflomiss.R: 7
> test-gflomiss.R: 8
> test-gflomiss.R: 9
> test-gflomiss.R: 10
> test-gflomiss.R: 11
> test-gflomiss.R: 12
> test-gflomiss.R: 13
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: 14
> test-gflomiss.R: 15
> test-gflomiss.R: 16
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-gmonkmiss.R: odegree3 odegree4 odegree5 odegree6
> test-gmonkmiss.R: 1 5 7 5
> test-gmonkmiss.R: idegree2 idegree3 idegree4 idegree5 idegree6 idegree7 idegree8 idegree10
> test-gmonkmiss.R: 3 5 1 3 2 1 1 1
> test-gmonkmiss.R: idegree11
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 15
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-ergm.bridge.llr.R: 16
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-ergm.bridge.llr.R: 2
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.6245.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-gmonkmiss.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 5
> test-gmonkmiss.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-ergm.bridge.llr.R: 6
> test-gmonkmiss.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-gmonkmiss.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-gmonkmiss.R: Iteration 1 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:3.3e-34
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.4205.
> test-gmonkmiss.R: Iteration 2 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.8e-14
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.1501.
> test-gmonkmiss.R: Iteration 3 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2e-04
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 7
> test-gmonkmiss.R: The log-likelihood improved by 0.03536.
> test-gmonkmiss.R: Iteration 4 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.6e-01
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.007343.
> test-gmonkmiss.R: Iteration 5 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2.4e-01
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.00569.
> test-gmonkmiss.R: Iteration 6 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:9.9e-02
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.00904.
> test-gmonkmiss.R: Iteration 7 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:7.6e-01
> test-gmonkmiss.R: Convergence detected. Stopping.
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 8
> test-gmonkmiss.R: The log-likelihood improved by 0.001102.
> test-gmonkmiss.R: Finished CD.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.4514.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 15
> test-gmonkmiss.R: The log-likelihood improved by 0.0105.
> test-ergm.bridge.llr.R: 16
> test-gmonkmiss.R: Convergence test p-value: < 0.0001.
> test-gmonkmiss.R: Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.7035.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-gmonkmiss.R: Convergence test p-value: < 0.0001.
> test-gmonkmiss.R: Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 9
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-ergm.bridge.llr.R: 12
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4613.
> test-metrics.R: The log-likelihood improved by 4.1429.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.8364.
> test-metrics.R: The log-likelihood improved by 4.7215.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 1.1346.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1129.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-miss-dep.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-miss-dep.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0037.
> test-miss-dep.R: Convergence test P-value:4.6e-47
> test-miss-dep.R: 1
> test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss-dep.R: The log-likelihood improved by 1.824.
> test-miss-dep.R: Iteration 2 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.5e-23
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.6054.
> test-miss-dep.R: Iteration 3 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.1e-07
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.1283.
> test-miss-dep.R: Iteration 4 of at most 60:
> test-miss-dep.R: Convergence test P-value:3.3e-04
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.05435.
> test-miss-dep.R: Iteration 5 of at most 60:
> test-miss-dep.R: Convergence test P-value:2.1e-01
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.006185.
> test-miss-dep.R: Iteration 6 of at most 60:
> test-miss-dep.R: Convergence test P-value:4.1e-01
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.002664.
> test-miss-dep.R: Iteration 7 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4018.
> test-metrics.R: The log-likelihood improved by 3.2780.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.6e-01
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.007694.
> test-miss-dep.R: Iteration 8 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.9e-01
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.006878.
> test-miss-dep.R: Iteration 9 of at most 60:
> test-miss-dep.R: Convergence test P-value:7.8e-01
> test-miss-dep.R: Convergence detected. Stopping.
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.0003111.
> test-miss-dep.R: Finished CD.
> test-miss-dep.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.6020.
> test-metrics.R: The log-likelihood improved by 3.4584.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: Post-burnin sample is constant; returning.
> test-miss-dep.R: 1
> test-miss-dep.R: Optimizing with step length 1.0000.
> test-miss-dep.R: The log-likelihood improved by 0.0017.
> test-miss-dep.R: Convergence test p-value: < 0.0001.
> test-miss-dep.R: Converged with 99% confidence.
> test-miss-dep.R: Finished MCMLE.
> test-miss-dep.R: Evaluating log-likelihood at the estimate.
> test-miss-dep.R: Setting up bridge sampling...
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.2132.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: Using 16 bridges: 1
> test-miss-dep.R: 2
> test-miss-dep.R: 3
> test-miss-dep.R: 4
> test-miss-dep.R: 5
> test-miss-dep.R: 6
> test-miss-dep.R: 7
> test-miss-dep.R: 8
> test-miss-dep.R: 9
> test-miss-dep.R: 10
> test-miss-dep.R: 11
> test-miss-dep.R: 12
> test-miss-dep.R: 13
> test-miss-dep.R: 14
> test-miss-dep.R: 15
> test-miss-dep.R: 16
> test-miss-dep.R: .
> test-miss-dep.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-miss-dep.R: model likelihood is only implemented for dyad-independent constraints
> test-miss-dep.R: at this time. Number of observations is similarly poorly defined. This
> test-miss-dep.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-miss-dep.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-miss-dep.R: distribution and constraints.
> test-miss-dep.R:
> test-miss-dep.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss-dep.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0377.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: n=20, density=0.1, missing=0.1
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:3e-13
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.2096.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-12
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1974.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:5.8e-11
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.341.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4e-15
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1744.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-16
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6862.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.3e-15
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.3025.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-19
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1888.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4158.
> test-miss.CD.R: Convergence test P-value:2.3e-17
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.2862.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-metrics.R: The log-likelihood improved by 1.9115.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.6e-07
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.2007.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.1e-02
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.03854.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-05
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1259.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.0004877.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 0.4804.
> test-metrics.R: The log-likelihood improved by 2.5519.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.5e-68
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.7099.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-miss.CD.R: Convergence test P-value:1.6e-64
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.5925.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-metrics.R: The log-likelihood improved by 2.9415.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.6e-53
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6327.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.1e-37
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.8025.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-30
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.583.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-19
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6591.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.2e-04
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.05663.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.4e-01
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.007268.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.1e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1226.
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.3e-54
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.5854.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.4e-52
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6164.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.5e-46
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6486.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-32
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 1.361.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.8e-14
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.379.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-03
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.05118.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.9e-02
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.03478.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.5e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.001186.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Network statistics:
> test-miss.CD.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.CD.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.CD.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.CD.R: 0 0 0 0 0 0 0
> test-miss.CD.R: Correct estimate = -2.028148
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-283
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by 1.712.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-235
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: 1
> test-metrics.R: 2
> test-metrics.R: 3 4
> test-metrics.R: 5
> test-metrics.R: 6
> test-metrics.R: 7
> test-metrics.R: 8
> test-metrics.R: 9 10
> test-metrics.R: 11 Optimizing with step length 0.3934.
> test-metrics.R: The log-likelihood improved by 4.1457.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 1.493.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.3e-184
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by 1.167.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.9e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.8651.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-147
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.2e-155
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.8e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:1e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.3360.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:8e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.7e-158
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0827.
> test-metrics.R: Convergence test p-value: 0.0004.
> test-metrics.R: Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 13 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-156
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 14 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-162
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 15 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 16 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.1e-159
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: 1
> test-metrics.R: 2 3 4
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-metrics.R: 5 6
> test-miss.CD.R: Iteration 17 of at most 60:
> test-metrics.R: 7
> test-metrics.R: 8 9
> test-metrics.R: 10 11
> test-metrics.R: Optimizing with step length 0.3701.
> test-metrics.R: The log-likelihood improved by 2.3554.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:3.3e-159
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 18 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.6e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 19 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-163
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 20 of at most 60:
> test-metrics.R: 1 2
> test-metrics.R: 3 4 5 6
> test-metrics.R: 7
> test-metrics.R: 8 9 10
> test-metrics.R: 11
> test-metrics.R: 12 Optimizing with step length 0.5218.
> test-metrics.R: The log-likelihood improved by 2.9696.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:3.5e-156
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 21 of at most 60:
> test-metrics.R: 1
> test-miss.CD.R: Convergence test P-value:2.3e-153
> test-metrics.R: 2
> test-miss.CD.R: 1
> test-metrics.R: 3 Optimizing with step length 0.8048.
> test-miss.CD.R: 2
> test-metrics.R: The log-likelihood improved by 1.8226.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 22 of at most 60:
> test-miss.CD.R: Convergence test P-value:1e-157
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 23 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.2705.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-160
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 24 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0012.
> test-miss.CD.R: Convergence test P-value:2.1e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: Convergence test p-value: 0.0099.
> test-metrics.R: Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 25 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 26 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.5e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 27 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2 3
> test-metrics.R: 4
> test-metrics.R: 5
> test-metrics.R: 6
> test-metrics.R: 7 8
> test-metrics.R: 9 10 11
> test-metrics.R: 12 13
> test-metrics.R: Optimizing with step length 0.4397.
> test-metrics.R: The log-likelihood improved by 3.0119.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.8e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 28 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-160
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 29 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2
> test-metrics.R: 3
> test-metrics.R: 4
> test-metrics.R: 5 6
> test-metrics.R: 7 8
> test-metrics.R: 9 10 11
> test-metrics.R: 12
> test-metrics.R: Optimizing with step length 0.6225.
> test-miss.CD.R: Convergence test P-value:2.9e-157
> test-metrics.R: The log-likelihood improved by 3.6934.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 30 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 31 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 1.0488.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-miss.CD.R: Convergence test P-value:1.9e-155
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 32 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 33 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0821.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1e-153
> test-miss.CD.R: 1 2
> test-miss.R: n=20, density=0.1, missing=0.05
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 34 of at most 60:
> test-miss.R: Correct estimate =
> test-miss.R: -2.118156 with log-likelihood -120.6883 .
> test-miss.CD.R: Convergence test P-value:2.5e-165
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 35 of at most 60:
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R:
> test-miss.R: MPLE estimate = -2.118156 with log-likelihood -120.6883 OK.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.CD.R: Convergence test P-value:3.7e-164
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 36 of at most 60:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 26 dyads is required.
> test-miss.R: Imputing 3 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 41 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:3.5e-165
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 37 of at most 60:
> test-miss.CD.R: Convergence test P-value:3.8e-167
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 38 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:4.3e-166
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 39 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 49.45267
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2
> test-miss.R: 3 4
> test-miss.R: 5 6
> test-miss.R: 7 8
> test-miss.R: 9 10
> test-miss.R: 11 12
> test-miss.R: Optimizing with step length 0.4099.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.5936.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.374066
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.8e-166
> test-miss.CD.R: 1 2
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 40 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.36008
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3 4
> test-miss.R: 5
> test-miss.R: 6
> test-miss.R: 7
> test-miss.R: 8 9
> test-miss.R: 10 11
> test-miss.R: 12
> test-miss.R: Optimizing with step length 0.4981.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.2702.
> test-miss.R: Distance from origin on tolerance region scale: 192.073 (previously 422.077).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.647302
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.4e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 41 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.3e-139
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 42 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 17.50766
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4 5
> test-miss.R: Optimizing with step length 0.7736.
> test-miss.CD.R: Convergence test P-value:7.6e-160
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.0776.
> test-miss.R: Distance from origin on tolerance region scale: 71.38353 (previously 259.1767).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.95412
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 43 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:3e-152
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Back from constrained MCMC.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 44 of at most 60:
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 5.621399
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.3258.
> test-miss.R: Distance from origin on tolerance region scale: 6.488425 (previously 62.9371).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 5 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.070021
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:3.1e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 45 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.9e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 46 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 32.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 1.853909
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.CD.R: Convergence test P-value:8.8e-148
> test-miss.CD.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.CD.R: 2
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 47 of at most 60:
> test-miss.R: The log-likelihood improved by 0.0589.
> test-miss.R: Distance from origin on tolerance region scale: 1.17581 (previously 10.81058).
> test-miss.R: Test statistic: T^2 = 11.54078, with 1 free parameter(s) and 179.1884 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0008.
> test-miss.R: Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Initializing model to obtain the list of dyad-independent terms...
> test-miss.CD.R: Convergence test P-value:1.8e-153
> test-miss.CD.R: 1
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 48 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.8e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 49 of at most 60:
> test-miss.R: Dyad-independent submodel MLE has likelihood -120.6883 at:
> test-miss.R: [1] -2.118156 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.CD.R: Convergence test P-value:6e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 50 of at most 60:
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-2.133081, 0.000000].
> test-miss.R: Running theta=[-2.132118, 0.000000].
> test-miss.R: Running theta=[-2.131155, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.4e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.130192, 0.000000].
> test-miss.R: Running theta=[-2.129229, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 51 of at most 60:
> test-miss.R: Running theta=[-2.128267, 0.000000].
> test-miss.R: Running theta=[-2.127304, 0.000000].
> test-miss.R: Running theta=[-2.126341, 0.000000].
> test-miss.R: Running theta=[-2.125378, 0.000000].
> test-miss.R: Running theta=[-2.124415, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.2e-150
> test-miss.R: Running theta=[-2.123452, 0.000000].
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.122489, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 52 of at most 60:
> test-miss.R: Running theta=[-2.121526, 0.000000].
> test-miss.R: Running theta=[-2.120563, 0.000000].
> test-miss.R: Running theta=[-2.1196, 0.0000].
> test-miss.R: Running theta=[-2.118637, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.009575496) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.119005, 0.000000].
> test-miss.R: Running theta=[-2.119968, 0.000000].
> test-miss.R: Running theta=[-2.120931, 0.000000].
> test-miss.R: Running theta=[-2.121894, 0.000000].
> test-miss.R: Running theta=[-2.122857, 0.000000].
> test-miss.CD.R: Convergence test P-value:2.2e-161
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.12382, 0.00000].
> test-miss.R: Running theta=[-2.124783, 0.000000].
> test-miss.R: Running theta=[-2.125746, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 53 of at most 60:
> test-miss.R: Running theta=[-2.126709, 0.000000].
> test-miss.R: Running theta=[-2.127671, 0.000000].
> test-miss.R: Running theta=[-2.128634, 0.000000].
> test-miss.R: Running theta=[-2.129597, 0.000000].
> test-miss.R: Running theta=[-2.13056, 0.00000].
> test-miss.R: Running theta=[-2.131523, 0.000000].
> test-miss.CD.R: Convergence test P-value:5.8e-145
> test-miss.R: Running theta=[-2.132486, 0.000000].
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.133449, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.007542247) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132854, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 54 of at most 60:
> test-miss.R: Running theta=[-2.131891, 0.000000].
> test-miss.R: Running theta=[-2.130928, 0.000000].
> test-miss.R: Running theta=[-2.129965, 0.000000].
> test-miss.R: Running theta=[-2.129002, 0.000000].
> test-miss.R: Running theta=[-2.128039, 0.000000].
> test-miss.R: Running theta=[-2.127076, 0.000000].
> test-miss.CD.R: Convergence test P-value:5.8e-163
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.126113, 0.000000].
> test-miss.R: Running theta=[-2.125151, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Running theta=[-2.124188, 0.000000].
> test-miss.CD.R: Iteration 55 of at most 60:
> test-miss.R: Running theta=[-2.123225, 0.000000].
> test-miss.R: Running theta=[-2.122262, 0.000000].
> test-miss.R: Running theta=[-2.121299, 0.000000].
> test-miss.R: Running theta=[-2.120336, 0.000000].
> test-miss.R: Running theta=[-2.119373, 0.000000].
> test-miss.R: Running theta=[-2.11841, 0.00000].
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.006188692) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.118778, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 56 of at most 60:
> test-miss.R: Running theta=[-2.119741, 0.000000].
> test-miss.R: Running theta=[-2.120704, 0.000000].
> test-miss.R: Running theta=[-2.121667, 0.000000].
> test-miss.R: Running theta=[-2.12263, 0.00000].
> test-miss.CD.R: Convergence test P-value:2e-150
> test-miss.R: Running theta=[-2.123593, 0.000000].
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.124555, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 57 of at most 60:
> test-miss.R: Running theta=[-2.125518, 0.000000].
> test-miss.R: Running theta=[-2.126481, 0.000000].
> test-miss.R: Running theta=[-2.127444, 0.000000].
> test-miss.R: Running theta=[-2.128407, 0.000000].
> test-miss.R: Running theta=[-2.12937, 0.00000].
> test-miss.R: Running theta=[-2.130333, 0.000000].
> test-miss.R: Running theta=[-2.131296, 0.000000].
> test-miss.R: Running theta=[-2.132259, 0.000000].
> test-miss.R: Running theta=[-2.133222, 0.000000].
> test-miss.CD.R: Convergence test P-value:2.4e-151
> test-miss.CD.R: 1 2
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005396227) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132626, 0.000000].
> test-miss.R: Running theta=[-2.131664, 0.000000].
> test-miss.R: Running theta=[-2.130701, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 58 of at most 60:
> test-miss.R: Running theta=[-2.129738, 0.000000].
> test-miss.R: Running theta=[-2.128775, 0.000000].
> test-miss.R: Running theta=[-2.127812, 0.000000].
> test-miss.R: Running theta=[-2.126849, 0.000000].
> test-miss.R: Running theta=[-2.125886, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.2e-168
> test-miss.R: Running theta=[-2.124923, 0.000000].
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.12396, 0.00000].
> test-miss.R: Running theta=[-2.122997, 0.000000].
> test-miss.R: Running theta=[-2.122035, 0.000000].
> test-miss.R: Running theta=[-2.121072, 0.000000].
> test-miss.R: Running theta=[-2.120109, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 59 of at most 60:
> test-miss.R: Running theta=[-2.119146, 0.000000].
> test-miss.R: Running theta=[-2.118183, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:3e-159
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 60 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.5e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1728:32768
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 486
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 1.8539 5.6566 0.2566 0.4463
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -8.881 -1.881 2.119 5.119 14.119
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R:
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -2.129e-17 1.663e+00 1.067e-01 1.067e-01
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.8807 -1.3807 0.1193 1.1193 4.0693
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. 1.853909e+00 NA
> test-miss.R: test stat. 4.040502e+00 1.632566e+01
> test-miss.R: P-val. 5.333689e-05 7.904382e-05
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 0.50229634
> test-miss.R: Lag 128 0.27968825
> test-miss.R: Lag 192 0.14989734
> test-miss.R: Lag 256 0.08553620
> test-miss.R: Lag 320 0.01488518
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.04999730
> test-miss.R: Lag 128 0.05488745
> test-miss.R: Lag 192 -0.03080175
> test-miss.R: Lag 256 0.03734623
> test-miss.R: Lag 320 0.01289319
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.4658741
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.6413056
> test-miss.R: Joint P-value (lower = worse): 0.5503774
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.2553269
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.7984706
> test-miss.R: Joint P-value (lower = worse): 0.5503774 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -2.133563 with log-likelihood -120.7039 OK.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: Correct estimate = -1.663142 with log-likelihood -79.82064 .
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R:
> test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 8 dyads is required.
> test-miss.R: Imputing 1 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 30 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.15638
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3
> test-miss.R: 4 5
> test-miss.R: 6 7 8
> test-miss.R: Optimizing with step length 0.5368.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 4.3000.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.146333
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 14.85185
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 3.2552.
> test-miss.R: Distance from origin on tolerance region scale: 64.83604 (previously 323.1391).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.584689
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.600823
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1297.
> test-miss.R: Distance from origin on tolerance region scale: 2.582218 (previously 84.20395).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.684393
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.2386831
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0011.
> test-miss.R: Distance from origin on tolerance region scale: 0.02175454 (previously 2.583022).
> test-miss.R: Test statistic: T^2 = 16.95471, with 1 free parameter(s) and 192.1073 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -79.82064 at:
> test-miss.R: [1] -1.663142 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-1.674863, 0.000000].
> test-miss.R: Running theta=[-1.674107, 0.000000].
> test-miss.R: Running theta=[-1.673351, 0.000000].
> test-miss.R: Running theta=[-1.672594, 0.000000].
> test-miss.R: Running theta=[-1.671838, 0.000000].
> test-miss.R: Running theta=[-1.671082, 0.000000].
> test-miss.R: Running theta=[-1.670326, 0.000000].
> test-miss.R: Running theta=[-1.66957, 0.00000].
> test-miss.R: Running theta=[-1.668813, 0.000000].
> test-miss.R: Running theta=[-1.668057, 0.000000].
> test-miss.R: Running theta=[-1.667301, 0.000000].
> test-miss.R: Running theta=[-1.666545, 0.000000].
> test-miss.R: Running theta=[-1.665789, 0.000000].
> test-miss.R: Running theta=[-1.665033, 0.000000].
> test-miss.R: Running theta=[-1.664276, 0.000000].
> test-miss.R: Running theta=[-1.66352, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005483681) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-1.663809, 0.000000].
> test-miss.R: Running theta=[-1.664565, 0.000000].
> test-miss.R: Running theta=[-1.665321, 0.000000].
> test-miss.R: Running theta=[-1.666078, 0.000000].
> test-miss.R: Running theta=[-1.666834, 0.000000].
> test-miss.R: Running theta=[-1.66759, 0.00000].
> test-miss.R: Running theta=[-1.668346, 0.000000].
> test-miss.R: Running theta=[-1.669102, 0.000000].
> test-miss.R: Running theta=[-1.669858, 0.000000].
> test-miss.R: Running theta=[-1.670615, 0.000000].
> test-miss.R: Running theta=[-1.671371, 0.000000].
> test-miss.R: Running theta=[-1.672127, 0.000000].
> test-miss.R: Running theta=[-1.672883, 0.000000].
> test-miss.R: Running theta=[-1.673639, 0.000000].
> test-miss.R: Running theta=[-1.674396, 0.000000].
> test-miss.R: Running theta=[-1.675152, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.2387 5.2156 0.3346 0.3867
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -9.2346 -4.2346 -0.2346 2.7654 11.7154
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 4.040e-17 1.007e+00 6.463e-02 6.463e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -1.2346 -0.7346 -0.2346 0.7654 2.7154
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R:
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.2386831 NA
> test-miss.R: test stat. -0.6087923 0.3706280
> test-miss.R: P-val. 0.5426621 0.5433813
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.000000000
> test-miss.R: Lag 128 0.141730657
> test-miss.R: Lag 256 -0.008044799
> test-miss.R: Lag 384 0.039503814
> test-miss.R: Lag 512 0.016265240
> test-miss.R: Lag 640 0.025525909
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.03837238
> test-miss.R: Lag 128 0.06102163
> test-miss.R: Lag 192 0.01817600
> test-miss.R: Lag 256 -0.07663989
> test-miss.R: Lag 320 -0.02107378
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -1.387683
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.1652336
> test-miss.R: Joint P-value (lower = worse): 0.2706448
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain
> test-miss.R: 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.5702328
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.5685198
> test-miss.R: Joint P-value (lower = worse): 0.2706448 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -1.675241 with log-likelihood -79.81794 OK.
> test-miss.R: Correct estimate = -3.157 with log-likelihood -8.355963 .
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R:
> test-miss.R: MPLE estimate = -3.157 with log-likelihood -8.355963 OK.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 2 dyads is required.
> test-miss.R: Imputing 0 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 2 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.942387
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.8416.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.729057
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 0.9012346
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1847.
> test-miss.R: Distance from origin on tolerance region scale: 3.678483 (previously 39.20962).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -3.138904
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.05349794
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0008.
> test-miss.R: Distance from origin on tolerance region scale: 0.01512904 (previously 4.293514).
> test-miss.R: Test statistic: T^2 = 22.84874, with 1 free parameter(s) and 256.603 degrees of freedom.
> test-miss.R: Convergence test p-value: < 0.0001.
> test-miss.R: Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -8.355963 at:
> test-miss.R: [1] -3.157 0.000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-3.11196, 0.00000].
> test-miss.R: Running theta=[-3.114866, 0.000000].
> test-miss.R: Running theta=[-3.117772, 0.000000].
> test-miss.R: Running theta=[-3.120678, 0.000000].
> test-miss.R: Running theta=[-3.123584, 0.000000].
> test-miss.R: Running theta=[-3.126489, 0.000000].
> test-miss.R: Running theta=[-3.129395, 0.000000].
> test-miss.R: Running theta=[-3.132301, 0.000000].
> test-miss.R: Running theta=[-3.135207, 0.000000].
> test-miss.R: Running theta=[-3.138113, 0.000000].
> test-miss.R: Running theta=[-3.141018, 0.000000].
> test-miss.R: Running theta=[-3.143924, 0.000000].
> test-miss.R: Running theta=[-3.14683, 0.00000].
> test-miss.R: Running theta=[-3.149736, 0.000000].
> test-miss.R: Running theta=[-3.152642, 0.000000].
> test-miss.R: Running theta=[-3.155548, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 3584:65536
> test-miss.R: Thinning interval = 256
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.05350 1.39509 0.08949 0.08949
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.05761 -1.05761 -0.05761 0.94239 2.94239
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 9.838e-19 2.335e-01 1.498e-02 1.763e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -0.05761 -0.05761 -0.05761 -0.05761 0.94239
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.05349794 NA
> test-miss.R: test stat. -0.58650248 0.3476007
> test-miss.R: P-val. 0.55753790 0.5559932
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 256 -0.07640761
> test-miss.R: Lag 512 0.04674441
> test-miss.R: Lag 768 -0.08703223
> test-miss.R: Lag 1024 -0.01273911
> test-miss.R: Lag 1280 0.08918131
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 128 0.01440843
> test-miss.R: Lag 256 -0.06163854
> test-miss.R: Lag 384 -0.05752332
> test-miss.R: Lag 512 0.09381586
> test-miss.R: Lag 640 0.16935966
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 1.222791
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.2214088
> test-miss.R: Joint P-value (lower = worse): 0.530107
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 0.899493
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.3683901
> test-miss.R: Joint P-value (lower = worse): 0.530107 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -3.110507 with log-likelihood -8.357166 OK.
> test-miss.R: Network statistics:
> test-miss.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.R: 0 0 0 0 0 0 0
> test-miss.R: Correct estimate = -2.028148
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Iteration 1 of at most 5:
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: 10
> test-miss.R: 11
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 2 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 3 of at most 5:
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6 7
> test-miss.R: 8 9
> test-miss.R: 10
> test-miss.R: 11
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 4 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4 5
> test-miss.R: 6
> test-miss.R: 7 8
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 5 of at most 5:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
> test-miss.R: MCMLE estimation did not converge after 5 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Using 16 bridges:
> test-miss.R: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6
> test-miss.R: 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: 10
> test-miss.R: 11
> test-miss.R: 12
> test-miss.R: 13
> test-miss.R: 14
> test-miss.R: 15
> test-miss.R: 16
> test-miss.R: .
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-largenetwork.R:
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-offset.R: Obtaining the responsible dyads.
> test-mple-offset.R: Evaluating the predictor and response matrix.
> test-mple-offset.R: Maximizing the pseudolikelihood.
> test-mple-offset.R: Finished MPLE.
> test-mple-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-offset.R: Iteration 1 of at most 60:
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-offset.R: 1
> test-mple-offset.R: Optimizing with step length 1.0000.
> test-mple-offset.R: The log-likelihood improved by 0.0040.
> test-mple-offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-mple-offset.R: Finished MCMLE.
> test-mple-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-mple-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-target.R: [1] 350 50 250
> test-mple-target.R: Structural check:
> test-mple-target.R: Mean degree: 1.4 .
> test-mple-target.R: Average degree among nodes with degree 2 or higher: 2.25 .
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-cov.R: Estimating Bootstrap Standard Errors using 500 simulated networks.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-target.R: Iteration 1 of at most 60:
> test-networkLite.R: Loading required package: networkLite
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 1.0000.
> test-nodrop.R: The log-likelihood improved by 0.0005.
> test-nodrop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-nodrop.R: Finished MCMLE.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Finished MPLE.
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-nodrop.R: 1
> test-networkLite.R: 4
> test-nodrop.R: Optimizing with step length 0.2247.
> test-nodrop.R: The log-likelihood improved by 2.2822.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-networkLite.R: 5 6
> test-networkLite.R: 7 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.2817.
> test-nodrop.R: The log-likelihood improved by 2.4734.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.2675.
> test-nodrop.R: The log-likelihood improved by 3.3752.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.3098.
> test-nodrop.R: The log-likelihood improved by 2.4783.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Convergence test p-value: 0.0001.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonident-test.R: 1
> test-nonident-test.R: Optimizing with step length 1.0000.
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-networkLite.R: 1 2
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-networkLite.R: 3
> test-networkLite.R: 4 5 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nonident-test.R: 1
> test-nonident-test.R: Optimizing with step length 0.8147.
> test-networkLite.R: 1
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-networkLite.R: Optimizing with step length 1.0000.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3 4 5
> test-networkLite.R: 6
> test-networkLite.R: 7 8 9 10
> test-networkLite.R: 11 Optimizing with step length 1.0000.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-nonunique-names.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonunique-names.R: Obtaining the responsible dyads.
> test-nonunique-names.R: Evaluating the predictor and response matrix.
> test-nonunique-names.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Convergence test p-value: 0.0019.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-nonunique-names.R: Finished MPLE.
> test-nonunique-names.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonunique-names.R: Iteration 1 of at most 1:
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2 3
> test-networkLite.R: 4 5
> test-networkLite.R: 6
> test-networkLite.R: 7 8
> test-networkLite.R: 9
> test-networkLite.R: 10 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-networkLite.R: Convergence test p-value: 0.0019.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonunique-names.R: 1 Optimizing with step length 1.0000.
> test-nonunique-names.R: The log-likelihood improved by 0.0084.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-nonunique-names.R: Convergence test p-value: 0.0480. Not converged with 99% confidence; increasing sample size.
> test-nonunique-names.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonunique-names.R: Finished MCMLE.
> test-nonunique-names.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonunique-names.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonunique-names.R: Sample statistics summary:
> test-nonunique-names.R:
> test-nonunique-names.R: Iterations = 2304:44032
> test-nonunique-names.R: Thinning interval = 128
> test-nonunique-names.R: Number of chains = 1
> test-nonunique-names.R: Sample size per chain = 327
> test-nonunique-names.R:
> test-nonunique-names.R: 1. Empirical mean and standard deviation for each variable,
> test-nonunique-names.R: plus standard error of the mean:
> test-nonunique-names.R:
> test-nonunique-names.R: Mean SD Naive SE Time-series SE
> test-nonunique-names.R: edgecov.a -0.2171 3.380 0.1869 0.3114
> test-nonunique-names.R: edgecov.a 0.1346 3.565 0.1971 0.4311
> test-nonunique-names.R:
> test-nonunique-names.R: 2. Quantiles for each variable:
> test-nonunique-names.R:
> test-nonunique-names.R: 2.5% 25% 50% 75% 97.5%
> test-nonunique-names.R: edgecov.a -7 -2 0 2 6.85
> test-nonunique-names.R: edgecov.a -7 -2 0 3 7.00
> test-nonunique-names.R:
> test-nonunique-names.R:
> test-nonunique-names.R: Are sample statistics significantly different from observed?
> test-networkLite.R: 1
> test-nonunique-names.R: edgecov.a edgecov.a (Omni)
> test-nonunique-names.R: diff. -0.2171254 0.1345566 NA
> test-nonunique-names.R: test stat. -0.6971750 0.3120903 1.2954084
> test-nonunique-names.R: P-val. 0.4856933 0.7549719 0.5307488
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics cross-correlations:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: edgecov.a 1.0000000 0.6853513
> test-nonunique-names.R: edgecov.a 0.6853513 1.0000000
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics auto-correlation:
> test-nonunique-names.R: Chain
> test-networkLite.R: Optimizing with step length 0.9410.
> test-nonunique-names.R: 1
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: Lag 0 1.000000000 1.00000000
> test-nonunique-names.R: Lag 128 0.592467538 0.65334235
> test-nonunique-names.R: Lag 256 0.298337375 0.41906307
> test-nonunique-names.R: Lag 384 0.082532656 0.22830518
> test-nonunique-names.R: Lag 512 -0.003477022 0.08811653
> test-nonunique-names.R: Lag 640 -0.090771181 0.03701357
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics burn-in diagnostic (Geweke):
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nonunique-names.R: Chain
> test-nonunique-names.R: 1
> test-nonunique-names.R:
> test-nonunique-names.R: Fraction in 1st window = 0.1
> test-nonunique-names.R: Fraction in 2nd window = 0.5
> test-nonunique-names.R:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: -0.41748721 0.04619135
> test-nonunique-names.R:
> test-nonunique-names.R: Individual P-values (lower = worse):
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: 0.6763221 0.9631577
> test-nonunique-names.R: Joint P-value (lower = worse): 0.8447246
> test-nonunique-names.R:
> test-nonunique-names.R: Note: MCMC diagnostics shown here are from the last round of
> test-nonunique-names.R: simulation, prior to computation of final parameter estimates.
> test-nonunique-names.R: Because the final estimates are refinements of those used for this
> test-nonunique-names.R: simulation run, these diagnostics may understate model performance.
> test-nonunique-names.R: To directly assess the performance of the final model on in-model
> test-nonunique-names.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-nonunique-names.R: GOF=~model).
> test-nonunique-names.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.6004.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-networkLite.R: Convergence test p-value: 0.0227.
> test-networkLite.R: Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.0061.
> test-offsets.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-offsets.R: Using 16 bridges: 1
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: 2
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-offsets.R: 3
> test-offsets.R: 4
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: 5
> test-offsets.R: 6
> test-offsets.R: 7
> test-offsets.R: 8
> test-offsets.R: 9
> test-offsets.R: 10
> test-offsets.R: 11
> test-offsets.R: 12
> test-offsets.R: 13
> test-offsets.R: 14
> test-offsets.R: 15
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-offsets.R: 16
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3 4
> test-networkLite.R: 5 6
> test-networkLite.R: 7
> test-networkLite.R: 8 9
> test-networkLite.R: 10 11
> test-networkLite.R: 12
> test-networkLite.R: 13
> test-networkLite.R: 14 15
> test-networkLite.R: 16
> test-networkLite.R: 17
> test-networkLite.R: 18
> test-networkLite.R: 19
> test-networkLite.R: 20
> test-networkLite.R: 21
> test-networkLite.R: 22
> test-networkLite.R: 23
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247.
> test-networkLite.R: Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-networkLite.R: Convergence test p-value: 0.0227.
> test-networkLite.R: Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10 11
> test-networkLite.R: 12 13
> test-networkLite.R: 14
> test-networkLite.R: 15 16
> test-networkLite.R: 17 18
> test-networkLite.R: 19
> test-networkLite.R: 20 21
> test-networkLite.R: 22 23
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8
> test-networkLite.R: 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458.
> test-networkLite.R: Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: The log-likelihood improved by 0.7959.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-offsets.R: The log-likelihood improved by 0.0207.
> test-operators.R: Finished MPLE.
> test-offsets.R: Convergence test p-value: 0.0005.
> test-offsets.R: Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-operators.R: Finished MPLE.
> test-offsets.R: Using 16 bridges:
> test-offsets.R: 1
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-offsets.R: 2
> test-offsets.R: 3
> test-offsets.R: 4
> test-offsets.R: 5
> test-offsets.R: 6
> test-offsets.R: 7
> test-offsets.R: 8
> test-parallel.R: parallel test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run.
> test-parallel.R: Skipping OpenMP test. This package installation was built without OpenMP support.
> test-offsets.R: 9
> test-offsets.R: 10
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-offsets.R: 11
> test-offsets.R: 12
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-offsets.R: 13
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-offsets.R: 14
> test-predict.ergm.R: Finished MPLE.
> test-offsets.R: 15
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-offsets.R: 16
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-runtime-diags.R: Obtaining the responsible dyads.
> test-runtime-diags.R: Evaluating the predictor and response matrix.
> test-runtime-diags.R: Maximizing the pseudolikelihood.
> test-runtime-diags.R: Finished MPLE.
> test-runtime-diags.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-runtime-diags.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: 1
> test-runtime-diags.R: Optimizing with step length 1.0000.
> test-runtime-diags.R: The log-likelihood improved by 0.0096.
> test-runtime-diags.R: Convergence test p-value: 0.0002. Converged with 99% confidence.
> test-runtime-diags.R: Finished MCMLE.
> test-runtime-diags.R: This model was fit using MCMC. To examine model diagnostics and check
> test-runtime-diags.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: 1
> test-scoping.R: Optimizing with step length 1.0000.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: The log-likelihood improved by 0.3512.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate.
> test-scoping.R: Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Using 16 bridges: 1
> test-scoping.R: 2
> test-scoping.R: 3
> test-scoping.R: 4
> test-scoping.R: 5
> test-scoping.R: 6
> test-scoping.R: 7
> test-scoping.R: 8
> test-scoping.R: 9
> test-scoping.R: 10
> test-scoping.R: 11
> test-scoping.R: 12
> test-scoping.R: 13
> test-scoping.R: 14
> test-scoping.R: 15
> test-scoping.R: 16
> test-scoping.R: .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-scoping.R: 1
> test-scoping.R: Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.3512.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate.
> test-scoping.R: Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges:
> test-scoping.R: 1
> test-scoping.R: 2
> test-scoping.R: 3
> test-scoping.R: 4
> test-scoping.R: 5
> test-scoping.R: 6
> test-scoping.R: 7
> test-scoping.R: 8
> test-scoping.R: 9
> test-scoping.R: 10
> test-scoping.R: 11
> test-scoping.R: 12
> test-scoping.R: 13
> test-scoping.R: 14
> test-scoping.R: 15
> test-scoping.R: 16
> test-scoping.R: .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-shrink-into-CH.R: 1
> test-shrink-into-CH.R: 2
> test-shrink-into-CH.R: 3
> test-shrink-into-CH.R: 4
> test-shrink-into-CH.R: 5
> test-shrink-into-CH.R: 6
> test-shrink-into-CH.R: 7
> test-shrink-into-CH.R: 8 9
> test-shrink-into-CH.R: 10
> test-shrink-into-CH.R: 11
> test-shrink-into-CH.R: 12
> test-shrink-into-CH.R: 13
> test-shrink-into-CH.R: 14
> test-shrink-into-CH.R: 15
> test-shrink-into-CH.R: 16
> test-shrink-into-CH.R: 17
> test-shrink-into-CH.R: 18
> test-shrink-into-CH.R: 19
> test-shrink-into-CH.R: 20
> test-shrink-into-CH.R: 1
> test-shrink-into-CH.R: 2
> test-shrink-into-CH.R: 3
> test-shrink-into-CH.R: 4
> test-shrink-into-CH.R: 5
> test-shrink-into-CH.R: 6
> test-shrink-into-CH.R: 7
> test-shrink-into-CH.R: 8
> test-shrink-into-CH.R: 9
> test-shrink-into-CH.R: 10
> test-shrink-into-CH.R: 11
> test-shrink-into-CH.R: 12
> test-shrink-into-CH.R: 13
> test-shrink-into-CH.R: 14
> test-shrink-into-CH.R: 15
> test-shrink-into-CH.R: 16
> test-shrink-into-CH.R: 17
> test-shrink-into-CH.R: 18
> test-shrink-into-CH.R: 19
> test-shrink-into-CH.R: 20
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-skip.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-skip.R: 1
> test-skip.R: Optimizing with step length 1.0000.
> test-skip.R: The log-likelihood improved by 0.0360.
> test-skip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-skip.R: Finished MCMLE.
> test-skip.R: This model was fit using MCMC. To examine model diagnostics and check
> test-skip.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-snctrl.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-snctrl.R: Obtaining the responsible dyads.
> test-snctrl.R: Evaluating the predictor and response matrix.
> test-snctrl.R: Maximizing the pseudolikelihood.
> test-snctrl.R: Finished MPLE.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.7009355 0.2208488
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.6617183 0.1405334
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0034.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.5333754 -0.1317716 0.6729982
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.57231795 -0.05712682 0.44962020
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0007.
> test-stocapprox.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.1e-111
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 1.945.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-44
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.5962.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:4e-07
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.06364.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.6e-05
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.04097.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.3e-03
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.01842.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.002083.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.743217 0.112619
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.7631980 0.1383531
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-98
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 1.862.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.5e-30
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.3427.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:3e-09
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.08204.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.9e-02
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.01313.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.2e-02
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.009411.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:2.6e-01
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.005336.
> test-stocapprox.R: Iteration 7 of at most 60:
> test-stocapprox.R: Convergence test P-value:7.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.0009177.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0022.
> test-stocapprox.R: Convergence test p-value: < 0.0001.
> test-stocapprox.R: Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R:
> test-stocapprox.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-stocapprox.R: * 'news(package="ergm.count")' for changes since last version
> test-stocapprox.R: * 'c
> test-stocapprox.R: itation("ergm.count")' for citation information
> test-stocapprox.R: * 'https://statnet.org' for help, support, and other information
> test-stocapprox.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 60:
> test-target-offset.R: 1 Optimizing with step length 1.0000.
> test-target-offset.R: The log-likelihood improved by 0.0210.
> test-target-offset.R: Convergence test p-value: 0.0002.
> test-target-offset.R: Converged with 99% confidence.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-target-offset.R: Using 16 bridges:
> test-target-offset.R: 1
> test-target-offset.R: 2
> test-target-offset.R: 3
> test-target-offset.R: 4
> test-target-offset.R: 5
> test-target-offset.R: 6
> test-target-offset.R: 7
> test-target-offset.R: 8
> test-target-offset.R: 9
> test-target-offset.R: 10
> test-target-offset.R: 11
> test-target-offset.R: 12
> test-target-offset.R: 13
> test-target-offset.R: 14
> test-target-offset.R: 15
> test-target-offset.R: 16
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 14336:262144
> test-target-offset.R: Thinning interval = 1024
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 0.55556 4.490 0.2880 0.2880
> test-target-offset.R: degree1 0.04527 2.005 0.1286 0.1286
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges -7 -2.5 0 3 9.00
> test-target-offset.R: degree1 -3 -1.0 0 1 4.95
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Are sample statistics significantly different from observed?
> test-target-offset.R: edges degree1 (Omni)
> test-target-offset.R: diff. 0.55555556 0.04526749 NA
> test-target-offset.R: test stat. 1.92893422 0.35200754 9.455405747
> test-target-offset.R: P-val. 0.05373903 0.72483261 0.009922641
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges degree1
> test-target-offset.R: edges 1.0000000 -0.7250142
> test-target-offset.R: degree1 -0.7250142 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges degree1
> test-target-offset.R: Lag 0 1.000000000 1.000000000
> test-target-offset.R: Lag 1024 -0.061427219 0.030194492
> test-target-offset.R: Lag 2048 0.007868535 0.092075103
> test-target-offset.R: Lag 3072 0.018624816 0.047810603
> test-target-offset.R: Lag 4096 -0.047471894 0.007659206
> test-target-offset.R: Lag 5120 -0.073593205 -0.009870131
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-target-offset.R: Chain
> test-target-offset.R: 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.4498910 -0.1601093
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.652789 0.872795
> test-target-offset.R: Joint P-value (lower = worse): 0.8380775
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-target-offset.R: Obtaining the responsible dyads.
> test-target-offset.R: Evaluating the predictor and response matrix.
> test-target-offset.R: Maximizing the pseudolikelihood.
> test-target-offset.R: Finished MPLE.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.7297.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 2 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.6389.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 3 of at most 3:
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.8386.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: MCMLE estimation did not converge after 3 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Using 16 bridges:
> test-target-offset.R: 1
> test-target-offset.R: 2
> test-target-offset.R: 3
> test-target-offset.R: 4
> test-target-offset.R: 5
> test-target-offset.R: 6
> test-target-offset.R: 7
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 8
> test-target-offset.R: 9
> test-target-offset.R: 10
> test-target-offset.R: 11
> test-target-offset.R: 12
> test-target-offset.R: 13
> test-target-offset.R: 14
> test-target-offset.R: 15
> test-target-offset.R: 16
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Sample statistics summary:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R:
> test-target-offset.R: Iterations = 3584:65536
> test-target-offset.R: Thinning interval = 256
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean
> test-target-offset.R: SD Naive SE Time-series SE
> test-target-offset.R: edges 12.77778 5.118432 0.32835 0.3283476
> test-target-offset.R: gwdegree 0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R: gwdegree.decay 0.01236 0.002961 0.00019 0.0001293
> test-target-offset.R: degree0 -0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges 3.000e+00 9.00000 13.00000 16.00000 23.00000
> test-target-offset.R: gwdegree 1.063e-12 1.00000 1.00000 1.00000 1.00000
> test-target-offset.R: gwdegree.decay 5.955e-03 0.01191 0.01191 0.01489 0.01489
> test-target-offset.R: degree0 -1.000e+00 -1.00000 -1.00000 -1.00000 0.00000
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: edges 1.0000000 0.2709648 0.6049364 -0.2709648
> test-target-offset.R: gwdegree 0.2709648 1.0000000 0.5143643 -1.0000000
> test-target-offset.R: gwdegree.decay 0.6049364 0.5143643 1.0000000 -0.5143643
> test-target-offset.R: degree0 -0.2709648 -1.0000000 -0.5143643 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: Lag 0 1.000000000 1.00000000 1.000000000 1.00000000
> test-target-offset.R: Lag 256 0.006056003 0.02865300 -0.034893817 0.02865300
> test-target-offset.R: Lag 512 0.062813023 -0.07862186 0.002608612 -0.07862186
> test-target-offset.R: Lag 768 -0.089332866 -0.07930006 -0.150790861 -0.07930006
> test-target-offset.R: Lag 1024 -0.091496281 -0.07564135 -0.189123113 -0.07564135
> test-target-offset.R: Lag 1280 0.030192390 0.03461402 0.073975025 0.03461402
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-target-offset.R: Chain 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.05663036 -1.34419649 0.38772965 1.34419649
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.9548397 0.1788849 0.6982161 0.1788849
> test-target-offset.R: Joint P-value (lower = worse): 0.3850888
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-term-Offset.R: 1
> test-term-Offset.R: Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0066.
> test-term-Offset.R: Convergence test p-value: < 0.0001.
> test-term-Offset.R: Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-term-Offset.R: 1 Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0024.
> test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
Saving _problems/test-term-Offset-21.R
Saving _problems/test-term-Offset-25.R
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-b12nodematch.R: In term 'b1nodematch' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b1dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1factor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1twostar' in package 'ergm': Argument 'base' has been superseded by 'levels2', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b2dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: In term 'asymmetric' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Observed statistic(s) ideg7+.homophily.group and ideg8+.homophily.group are at their smallest attainable values. Their coefficie
> test-term-directed.R: nts will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) gwodeg.fixed.0 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Observed statistic(s) edgecov.YearsTrusted are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-flexible.R: All terms are either offsets or extreme values. No optimization is performed.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: In term 'nodeifactor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Observed statistic(s) odeg7+ and odeg8+ are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) odeg6+.homophily.group, odeg7+.homophily.group, and odeg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R:
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Evaluating log-likelihood at the estimate.
> test-term-directed.R:
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-mm.R: Note: Term 'mm(~Grade >= 10, levels = -1)' skipped because it contributes no statistics.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R:
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R:
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: 1
> test-term-options.R: Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0013.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Fitting the dyad-independent submodel...
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-options.R: Bridging between the dyad-independent submodel and the full model...
> test-term-options.R: Setting up bridge sampling...
> test-term-options.R: Using 16 bridges: 1
> test-term-options.R: 2
> test-term-options.R: 3
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-options.R: 4
> test-term-options.R: 5
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: 6
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-options.R: 7
> test-term-options.R: 8
> test-term-options.R: 9
> test-term-options.R: 10
> test-term-options.R: 11
> test-term-options.R: 12
> test-term-options.R: 13
> test-term-options.R: 14
> test-term-options.R: 15
> test-term-options.R: 16
> test-term-options.R: .
> test-term-options.R: Bridging finished.
> test-term-options.R:
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-options.R: 1
> test-term-options.R: Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0003.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Evaluating log-likelihood at the estimate.
> test-term-gw-sp.R:
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'DiscUnif2' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-u-function.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'.
> test-valued-sim.R: mean=1, var=4, corr=0.3
> test-valued-sim.R: eta=(0.192307692307692,0.0824175824175824,0.362637362637363)
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated mean (stats only):0.9964445
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated means (target=1):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 1.0034478 0.9683903
> test-valued-sim.R: [2,] 1.0302079 NA 0.8923730
> test-valued-sim.R: [3,] 0.6870641 0.7628159 NA
> test-valued-sim.R: Simulated vars (target=4):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 3.863045 4.034701
> test-valued-sim.R: [2,] 3.944388 NA 3.888997
> test-valued-sim.R: [3,] 3.740890 4.202553 NA
> test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3):
> test-valued-sim.R: [1] 0.2781206 0.2324470 0.3420405
> test-valued-sim.R: ==== output='stats', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R:
> test-valued-sim.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-sim.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-sim.R: * 'c
> test-valued-sim.R: itation("ergm.count")' for citation information
> test-valued-sim.R: * 'https://statnet.org' for help, support, and other information
> test-valued-sim.R:
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R:
> test-valued-terms.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-terms.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-terms.R: * 'citation("ergm.count")' for citation information
> test-valued-terms.R: * 'https://statnet.org' for help, support, and other information
> test-valued-terms.R:
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• empty test (1):
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-term-Offset.R:19:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `off <- ergm(nw ~ edges + offset(triangle), offset.coef = 0.1)` to throw a message.
── Failure ('test-term-Offset.R:23:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `Off <- ergm(nw ~ edges + Offset(~triangle, which = 1, coef = 0.1))` to throw a message.
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 4.10.1
Flags: --no-vignettes
Check: whether package can be installed
Result: WARN
Found the following significant warnings:
Rd warning: no hsearch.rds meta data for package ergm
See ‘/home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/ergm.Rcheck/00install.out’ for details.
* used C compiler: ‘gcc-14 (Debian 14.3.0-10) 14.3.0’
Flavor: r-patched-linux-x86_64
Version: 4.10.1
Flags: --no-vignettes
Check: tests
Result: ERROR
Running ‘requireNamespaceTest.R’ [3s/3s]
Running ‘testthat.R’ [346s/187s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # File tests/testthat.R in package ergm, part of the Statnet suite of packages
> # for network analysis, https://statnet.org .
> #
> # This software is distributed under the GPL-3 license. It is free, open
> # source, and has the attribution requirements (GPL Section 7) at
> # https://statnet.org/attribution .
> #
> # Copyright 2003-2025 Statnet Commons
> ################################################################################
> library(testthat)
> library(ergm)
Loading required package: network
'network' 1.19.0 (2024-12-08), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4.10.1 (2025-08-26), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.
>
> test_check("ergm")
Starting 2 test processes.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5 6
> test-basis.R: 7
> test-basis.R: 8 9
> test-basis.R: 10
> test-basis.R: 11 12 13 14 15 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2 3 4
> test-basis.R: 5 6 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 12
> test-basis.R: 13 14 15 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-basis.R: 3 4 5 6 7
> test-basis.R: 8
> test-basis.R: 9 10
> test-basis.R: 11 12 13
> test-basis.R: 14 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1 The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1 The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1 The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1 The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: Attaching package: 'statnet.common'
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: The following objects are masked from 'package:base':
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: attr, order, replace
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-bridge-target.stats.R: Unable to match target stats. Using MCMLE estimation.
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-bridge-target.stats.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 1
> test-bridge-target.stats.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: The log-likelihood improved by 0.0219.
> test-bridge-target.stats.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-bridge-target.stats.R: Finished MCMLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 5 6 7 8
> test-basis.R: The log-likelihood improved by 0.0072.
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-bridge-target.stats.R: 12 13
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 14 15 16
> test-bridge-target.stats.R: .
> test-basis.R: Using 16 bridges: 1 2
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: This model was fit using MCMC. To examine model diagnostics and check
> test-bridge-target.stats.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: 3
> test-basis.R: 4
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-basis.R: 5 6 7
> test-basis.R: 8 9 10 11 12
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-basis.R: 13 14
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-basis.R: 15 16
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 2
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1 The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1 The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1 The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-bridge-target.stats.R: 10
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 11
> test-basis.R: Using 16 bridges: 1 2 3 4
> test-basis.R: 5
> test-bridge-target.stats.R: 12
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9 10 11
> test-bridge-target.stats.R: 13
> test-basis.R: 12
> test-basis.R: 13 14
> test-basis.R: 15
> test-bridge-target.stats.R: 14
> test-basis.R: 16 .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 15
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 16
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1 The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1 The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1 The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1 The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1 2
> test-bridge-target.stats.R: 9
> test-basis.R: 3
> test-basis.R: 4 5 6
> test-basis.R: 7
> test-bridge-target.stats.R: 10
> test-basis.R: 8 9 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13 14 15 16
> test-bridge-target.stats.R: 11
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Iteration 1 of at most 60:
> test-checkpointing.R: Saving state in '/home/hornik/tmp/scratch/Rtmp2lUN7g/file3576c643d3f80d_001.RData'.
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-checkpointing.R: The log-likelihood improved by 0.0213.
> test-checkpointing.R: Step length converged once. Increasing MCMC sample size.
> test-checkpointing.R: Iteration 2 of at most 60:
> test-checkpointing.R: Saving state in '/home/hornik/tmp/scratch/Rtmp2lUN7g/file3576c643d3f80d_002.RData'.
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-checkpointing.R: 1 Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-checkpointing.R: The log-likelihood improved by 0.0238.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: 14
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-checkpointing.R: Using 16 bridges: 1 2
> test-checkpointing.R: 3
> test-checkpointing.R: 4
> test-checkpointing.R: 5
> test-checkpointing.R: 6
> test-checkpointing.R: 7
> test-checkpointing.R: 8 9
> test-bridge-target.stats.R: 15
> test-checkpointing.R: 10
> test-checkpointing.R: 11
> test-checkpointing.R: 12
> test-bridge-target.stats.R: 16
> test-checkpointing.R: 13
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-checkpointing.R: 14 15
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-checkpointing.R: 16
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Resuming from state saved in '/home/hornik/tmp/scratch/Rtmp2lUN7g/file3576c643d3f80d_002.RData'.
> test-checkpointing.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-checkpointing.R: 1 Optimizing with step length 1.0000.
> test-checkpointing.R: The log-likelihood improved by 0.0145.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: 5
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-bridge-target.stats.R: 6
> test-checkpointing.R: Using 16 bridges: 1
> test-checkpointing.R: 2 3
> test-checkpointing.R: 4
> test-checkpointing.R: 5 6
> test-bridge-target.stats.R: 7
> test-checkpointing.R: 7 8
> test-checkpointing.R: 9
> test-checkpointing.R: 10
> test-checkpointing.R: 11
> test-checkpointing.R: 12 13
> test-bridge-target.stats.R: 8
> test-checkpointing.R: 14
> test-checkpointing.R: 15 16
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constrain-degrees-edges.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constrain-degrees-edges.R: Iteration 1 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:5.2e-06
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.05768.
> test-constrain-degrees-edges.R: Iteration 2 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:3.7e-03
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.09813.
> test-constrain-degrees-edges.R: Finished CD.
> test-constrain-degrees-edges.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-degrees-edges.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondInDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R:
> test-constrain-blockdiag.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-constrain-blockdiag.R: * 'news(package="ergm.count")' for changes since last version
> test-constrain-blockdiag.R: * 'citation("ergm.count")' for citation information
> test-constrain-blockdiag.R: * 'https://statnet.org' for help, support, and other information
> test-constrain-blockdiag.R:
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constrain-dind.R: Iteration 1 of at most 60:
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: 1
> test-constrain-dind.R: Optimizing with step length 1.0000.
> test-constrain-dind.R: The log-likelihood improved by 0.0020.
> test-constrain-dind.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constrain-dind.R: Finished MCMLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Fitting the dyad-independent submodel...
> test-constrain-dind.R: Bridging between the dyad-independent submodel and the full model...
> test-constrain-dind.R: Setting up bridge sampling...
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Using 16 bridges: 1
> test-constrain-dind.R: 2
> test-constrain-dind.R: 3 4
> test-constrain-dind.R: 5 6
> test-constrain-dind.R: 7
> test-constrain-dind.R: 8
> test-constrain-dind.R: 9
> test-constrain-dind.R: 10
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: 11 12 13
> test-constrain-dind.R: 14 15 16
> test-constrain-dind.R: .
> test-constrain-dind.R: Bridging finished.
> test-constrain-dind.R:
> test-constrain-dind.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-dind.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-drop.R: The log-likelihood improved by 0.0007.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R: Fitting the dyad-independent submodel...
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Using 16 bridges: 1 2 3
> test-drop.R: 4 5 6 7
> test-drop.R: 8
> test-drop.R: 9
> test-drop.R: 10 11
> test-drop.R: 12
> test-drop.R: 13
> test-drop.R: 14
> test-drop.R: 15
> test-drop.R: 16
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constraints.R: All terms are either offsets or extreme values. No optimization is performed.
> test-constraints.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges: 1
> test-constraints.R: 2
> test-constraints.R: 3
> test-constraints.R: 4
> test-constraints.R: 5
> test-constraints.R: 6
> test-constraints.R: 7
> test-constraints.R: 8
> test-constraints.R: 9
> test-constraints.R: 10
> test-constraints.R: 11
> test-constraints.R: 12
> test-constraints.R: 13
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by < 0.0001.
> test-constraints.R: 14
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Fitting the dyad-independent submodel...
> test-constraints.R: 15
> test-constraints.R: 16
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-constraints.R: .
> test-drop.R: Using 16 bridges: 1
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent
> test-constraints.R: constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constraints.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constraints.R: Iteration 1 of at most 60:
> test-constraints.R: Convergence test P-value:1.1e-05
> test-constraints.R: 1 The log-likelihood improved by 0.07919.
> test-constraints.R: Iteration 2 of at most 60:
> test-constraints.R: Convergence test P-value:3.7e-02
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.01687.
> test-constraints.R: Iteration 3 of at most 60:
> test-constraints.R: Convergence test P-value:7e-01
> test-constraints.R: Convergence detected. Stopping.
> test-constraints.R: 1 The log-likelihood improved by 0.0006029.
> test-constraints.R: Finished CD.
> test-constraints.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constraints.R: Iteration 1 of at most 60:
> test-drop.R: 2
> test-drop.R: 3
> test-drop.R: 4
> test-constraints.R: 1 Optimizing with step length 1.0000.
> test-constraints.R: The log-likelihood improved by 0.0263.
> test-constraints.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constraints.R: Finished MCMLE.
> test-constraints.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-drop.R: 5
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges: 1
> test-constraints.R: 2
> test-constraints.R: 3
> test-constraints.R: 4
> test-drop.R: 6
> test-constraints.R: 5
> test-constraints.R: 6
> test-constraints.R: 7
> test-constraints.R: 8 9
> test-drop.R: 7
> test-constraints.R: 10 11 12
> test-constraints.R: 13
> test-constraints.R: 14
> test-drop.R: 8
> test-constraints.R: 15 16
> test-constraints.R: .
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-constraints.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constraints.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: 9
> test-drop.R: 10
> test-drop.R: 11
> test-drop.R: 12
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-drop.R: 13
> test-drop.R: 14
> test-drop.R: 15
> test-drop.R: 16
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: Evaluating network in model.
> test-drop.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-drop.R: 'ergm:MH_SPDyad'.
> test-drop.R: Initializing model...
> test-drop.R: Model initialized.
> test-drop.R: Using initial method 'MPLE'.
> test-drop.R: Initial parameters provided by caller: None.
> test-drop.R: number of free parameters: 7
> test-drop.R: number of fixed parameters: 0
> test-drop.R: Observed statistic(s) triangle and kstar5 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Fitting initial model.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Density guard set to 10000 from an initial count of 3 edges.
> test-drop.R:
> test-drop.R: Iteration 1 of at most 3 with parameter:
> test-drop.R: edges triangle degree2 kstar2 kstar5
> test-drop.R: 1.313175 -Inf 6.009173 -4.163221 -Inf
> test-drop.R: gwdegree gwdegree.decay
> test-drop.R: -2.860022 1.666671
> test-drop.R: Starting unconstrained MCMC...
> test-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-drop.R: Back from unconstrained MCMC.
> test-drop.R: New interval = 512.
> test-drop.R: Average estimating function values:
> test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay
> test-drop.R: -0.29629630 -0.11934156 -0.11934156 -0.57005199 0.06446661
> test-drop.R: Starting MCMLE Optimization...
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: Using lognormal metric (see control.ergm function).
> test-drop.R: Optimizing loglikelihood
> test-drop.R: Starting MCMC s.e. computation.
> test-drop.R: The log-likelihood improved by 0.0385.
> test-drop.R: Estimated covariance matrix of the statistics has nullity 3. Effective parameter number adjusted to 2.
> test-drop.R: Test statistic: T^2 = 21.94136, with 2 free parameter(s) and 240.9959 degrees of freedom.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-drop.R: Fitting the dyad-independent submodel...
> test-drop.R: Dyad-independent submodel MLE has likelihood -11.02185 at:
> test-drop.R: [1] -2.639057 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
> test-drop.R: [8] 0.000000
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Initializing model and proposals...
> test-drop.R: Model and proposals initialized.
> test-drop.R: Using 16 bridges: Running theta=[-1.175967, -Inf, 6.177257,-4.435272, -Inf,-1.386658, 1.950625, 0.000000].
> test-drop.R: Running theta=[-1.270360, -Inf, 5.778724,-4.149126, -Inf,-1.297196, 1.824778, 0.000000].
> test-drop.R: Running theta=[-1.364753, -Inf, 5.380191,-3.862979, -Inf,-1.207735, 1.698931, 0.000000].
> test-drop.R: Running theta=[-1.459146, -Inf, 4.981659,-3.576833, -Inf,-1.118273, 1.573085, 0.000000].
> test-drop.R: Running theta=[-1.553539, -Inf, 4.583126,-3.290686, -Inf,-1.028811, 1.447238, 0.000000].
> test-drop.R: Running theta=[-1.6479316, -Inf, 4.1845933,-3.0045393, -Inf,-0.9393491, 1.3213911, 0.0000000].
> test-drop.R: Running theta=[-1.7423245, -Inf, 3.7860606,-2.7183927, -Inf,-0.8498873, 1.1955444, 0.0000000].
> test-drop.R: Running theta=[-1.8367174, -Inf, 3.3875279,-2.4322461, -Inf,-0.7604255, 1.0696976, 0.0000000].
> test-drop.R: Running theta=[-1.9311104, -Inf, 2.9889952,-2.1460995, -Inf,-0.6709636, 0.9438508, 0.0000000].
> test-drop.R: Running theta=[-2.0255033, -Inf, 2.5904625,-1.8599529, -Inf,-0.5815018, 0.8180040, 0.0000000].
> test-drop.R: Running theta=[-2.1198962, -Inf, 2.1919298,-1.5738063, -Inf,-0.4920400, 0.6921573, 0.0000000].
> test-drop.R: Running theta=[-2.2142891, -Inf, 1.7933971,-1.2876597, -Inf,-0.4025782, 0.5663105, 0.0000000].
> test-drop.R: Running theta=[-2.3086821, -Inf, 1.3948644,-1.0015131, -Inf,-0.3131164, 0.4404637, 0.0000000].
> test-drop.R: Running theta=[-2.4030750, -Inf, 0.9963317,-0.7153665, -Inf,-0.2236545, 0.3146169, 0.0000000].
> test-drop.R: Running theta=[-2.4974679, -Inf, 0.5977990,-0.4292199, -Inf,-0.1341927, 0.1887702, 0.0000000].
> test-drop.R: Running theta=[-2.59186086, -Inf, 0.19926635,-0.14307330, -Inf,-0.04473091, 0.06292339, 0.00000000].
> test-drop.R: .
> test-drop.R: Bridge sampling finished. Collating...
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (binary, valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ctriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ctriad (binary)
> test-ergm-term-doc.R: Cyclic triples
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: localtriangle(x) (binary)
> test-ergm-term-doc.R: Triangles within neighborhoods
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1) (binary)
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: opentriad (binary)
> test-ergm-term-doc.R: Open triads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: threetrail(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: threepath(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Three-trails
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: triangle(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: triangles(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangles
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: tripercent(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangle percentage
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ttriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ttriad (binary)
> test-ergm-term-doc.R: Transitive triples
> test-ergm-term-doc.R: Found 31 matching ergm terms:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Definitions for term(s) b2factor :
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the
> test-ergm-term-doc.R: unique values of the attr attribute. Each of these statistics
> test-ergm-term-doc.R: gives the number of times a node with that attribute in the second mode of
> test-ergm-term-doc.R: the network appears in an edge. The second mode of a bipartite network
> test-ergm-term-doc.R: object is sometimes known as the "event" mode.
> test-ergm-term-doc.R: Keywords: bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3factor' were found. Try searching with search='b3factor'instead.
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm.bridge.llr.R: 2
> test-ergm-term-doc.R: Found 48 matching ergm terms:
> test-ergm-term-doc.R: B(formula, form) (valued)
> test-ergm-term-doc.R: Wrap binary terms for use in valued models
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Impose a curved structure on term parameters
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Exp(formula) (valued)
> test-ergm-term-doc.R: Exponentiate a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: For(...) (valued)
> test-ergm-term-doc.R: A for operator for terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Label(formula, label, pos) (valued)
> test-ergm-term-doc.R: Modify terms' coefficient names
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Log(formula, log0=-1/sqrt(.Machine$double.eps)) (valued)
> test-ergm-term-doc.R: Take a natural logarithm of a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Prod(formulas, label) (valued)
> test-ergm-term-doc.R: A product (or an arbitrary power combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: S(formula, attrs) (valued)
> test-ergm-term-doc.R: Evaluation on an induced subgraph
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Sum(formulas, label) (valued)
> test-ergm-term-doc.R: A sum (or an arbitrary linear combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiff(attr, pow=1, form="sum") (valued)
> test-ergm-term-doc.R: Absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiffcat(attr, base=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Categorical absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atleast(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values greater than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atmost(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values less than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: attrcov(attr, mat, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate by attribute pairing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalties(threshold=0) (valued)
> test-ergm-term-doc.R: Cyclical ties
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Cyclical weights
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edgecov(x, attrname=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edges (valued)
> test-ergm-term-doc.R: nonzero (valued)
> test-ergm-term-doc.R: Number of edges in the network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: equalto(value=0, tolerance=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values equal to a specific value (within tolerance)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: greaterthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly greater than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ininterval(lower=-Inf, upper=+Inf, open=c(TRUE,TRUE)) (valued)
> test-ergm-term-doc.R: Number of dyads whose values are in an interval
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mm(attrs, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Mixing matrix cells and margins
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mutual(form="min",threshold=0) (valued)
> test-ergm-term-doc.R: Mutuality
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecov(attr, form="sum") (valued)
> test-ergm-term-doc.R: nodemain(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of undirected dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodefactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of in-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeifactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: match(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Uniform homophily and differential homophily
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of out-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeofactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: receiver(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Receiver effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sender(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Sender effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: smallerthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly smaller than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Undirected degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sum(pow=1) (valued)
> test-ergm-term-doc.R: Sum of dyad values (optionally taken to a power)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: transitiveweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Transitive weights
> test-ergm-term-doc.R: Found 4 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DiscUnif(a,b)
> test-ergm-term-doc.R: Discrete Uniform reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: StdNormal
> test-ergm-term-doc.R: Standard Normal reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Unif(a,b)
> test-ergm-term-doc.R: Continuous Uniform reference
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 1 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R: Definitions for term(s) Bernoulli :
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference: Specifies each
> test-ergm-term-doc.R: dyad's baseline distribution to be Bernoulli with probability of
> test-ergm-term-doc.R: the tie being 0.5 . This is the only reference measure used
> test-ergm-term-doc.R: in binary mode.
> test-ergm-term-doc.R: Keywords: binary, discrete, finite, nonnegative
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'Cernoulli' were found. Try searching with search='Cernoulli'instead.
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrees
> test-ergm-term-doc.R: Preserve the receiver degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 16 matching ergm terms:
> test-ergm-term-doc.R: Dyads(fix=NULL, vary=NULL)
> test-ergm-term-doc.R: Constrain fixed or varying dyad-independent terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blockdiag(attr)
> test-ergm-term-doc.R: Block-diagonal structure constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blocks(attr=NULL, levels=NULL, levels2=FALSE, b1levels=NULL, b2levels=NULL)
> test-ergm-term-doc.R: Constrain blocks of dyads defined by mixing type on a vertex attribute.
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise(p01, p10)
> test-ergm-term-doc.R: A soft constraint to adjust the sampled distribution for
> test-ergm-term-doc.R: dyad-level noise with known perturbation probabilities
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: egocentric(attr=NULL, direction="both")
> test-ergm-term-doc.R: Preserve values of dyads incident on vertices with given attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixallbut(free.dyads)
> test-ergm-term-doc.R: Preserve the dyad status in all but the given edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixedas(fixed.dyads, present, absent)
> test-ergm-term-doc.R: Fix specific dyads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: hamming
> test-ergm-term-doc.R: Preserve the hamming distance to the given network (BROKEN: Do NOT Use)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: observed
> test-ergm-term-doc.R: Preserve the observed dyads of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Definitions for term(s) b1degrees :
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks: For bipartite networks, preserve the degree for the first mode of each vertex of the given
> test-ergm-term-doc.R: network, while allowing the degree for the second mode to vary.
> test-ergm-term-doc.R: Keywords: bipartite
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3degrees' were found. Try searching with search='b3degrees'instead.
> test-ergm.bridge.llr.R: 3
> test-ergm-term-doc.R: Found 2 matching ergm proposals:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R: Found 5 matching ergm proposals:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DistRLE
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Found 0 matching ergm proposals:
> test-ergm-term-doc.R: Found 18 matching ergm proposals:
> test-ergm-term-doc.R: BDStratTNT
> test-ergm-term-doc.R: TNT proposal with degree bounds, stratification, and a blocks constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegree
> test-ergm-term-doc.R: MHp for degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeDist
> test-ergm-term-doc.R: MHp for degreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeMix
> test-ergm-term-doc.R: MHp for degree mix constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegree
> test-ergm-term-doc.R: MHp for idegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegreeDist
> test-ergm-term-doc.R: MHp for idegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegree
> test-ergm-term-doc.R: MHp for odegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegreeDist
> test-ergm-term-doc.R: MHp for odegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingConstantEdges
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoiseTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm.bridge.llr.R: 4
> test-ergm-term-doc.R: Definitions for proposal(s) randomtoggle :
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Reference: Bernoulli Class: cross-sectional
> test-ergm-term-doc.R: May Enforce: .dyads bd
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No proposals named 'mandomtoggle' were found. Try searching with search='mandomtoggle'instead.
> test-ergm.bridge.llr.R: 5
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-term-doc.R: * 'c
> test-ergm-term-doc.R: itation("ergm.count")' for citation information
> test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-term-doc.R:
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergmMPLE.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-ergmMPLE.R: Obtaining the responsible dyads.
> test-ergmMPLE.R: Evaluating the predictor and response matrix.
> test-ergmMPLE.R: Maximizing the pseudolikelihood.
> test-ergmMPLE.R: Finished MPLE.
> test-ergmMPLE.R: Evaluating log-likelihood at the estimate.
> test-ergmMPLE.R:
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R:
> test-ergm.bridge.llr.R: 2
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R:
> test-ergm.bridge.llr.R: 3
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: 1 Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: The log-likelihood improved by 0.0001.
> test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: 15
> test-gflomiss.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: 2
> test-gflomiss.R: 3
> test-ergm.bridge.llr.R: .
> test-gflomiss.R: 4
> test-gflomiss.R: 5
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-gflomiss.R: 6
> test-gflomiss.R: 7
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gflomiss.R: 8
> test-gflomiss.R: 9
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gflomiss.R: 10 11
> test-ergm.bridge.llr.R: 2
> test-gflomiss.R: 12
> test-ergm.bridge.llr.R: 3
> test-gflomiss.R: 13
> test-gflomiss.R: 14
> test-ergm.bridge.llr.R: 4
> test-gflomiss.R: 15
> test-ergm.bridge.llr.R: 5
> test-gflomiss.R: 16
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 6
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-gflomiss.R: 1 Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: The log-likelihood improved by 0.0172.
> test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: 15
> test-gflomiss.R: Using 16 bridges: 1
> test-gflomiss.R: 2
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: 3
> test-gflomiss.R: 4
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-gflomiss.R: 5 6
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gflomiss.R: 7 8
> test-gflomiss.R: 9
> test-gflomiss.R: 10
> test-gflomiss.R: 11
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gflomiss.R: 12 13
> test-gflomiss.R: 14 15
> test-gflomiss.R: 16
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 2
> test-gmonkmiss.R: odegree3 odegree4 odegree5 odegree6
> test-gmonkmiss.R: 1 5 7 5
> test-gmonkmiss.R: idegree2 idegree3 idegree4 idegree5 idegree6 idegree7 idegree8 idegree10
> test-gmonkmiss.R: 3 5 1 3 2 1 1 1
> test-gmonkmiss.R: idegree11
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 3
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-ergm.bridge.llr.R: 4
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.6245.
> test-ergm.bridge.llr.R: 8
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-ergm.bridge.llr.R: 11
> test-gmonkmiss.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-gmonkmiss.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-gmonkmiss.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-gmonkmiss.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-gmonkmiss.R: Iteration 1 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:3.3e-34
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 12
> test-gmonkmiss.R: The log-likelihood improved by 0.4205.
> test-gmonkmiss.R: Iteration 2 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.8e-14
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.1501.
> test-gmonkmiss.R: Iteration 3 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2e-04
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.03536.
> test-gmonkmiss.R: Iteration 4 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.6e-01
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.007343.
> test-gmonkmiss.R: Iteration 5 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2.4e-01
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.00569.
> test-gmonkmiss.R: Iteration 6 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:9.9e-02
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.00904.
> test-gmonkmiss.R: Iteration 7 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:7.6e-01
> test-gmonkmiss.R: Convergence detected. Stopping.
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.001102.
> test-gmonkmiss.R: Finished CD.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: .
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.4514.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0105.
> test-ergm.bridge.llr.R: 5
> test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-ergm.bridge.llr.R: 6
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-ergm.bridge.llr.R: 7
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.7035.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 14
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges:
> test-ergm.bridge.llr.R: 1
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-metrics.R: 1 Optimizing with step length 0.4613.
> test-metrics.R: The log-likelihood improved by 4.1429.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.8364.
> test-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-metrics.R: The log-likelihood improved by 4.7215.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-miss-dep.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss-dep.R: Convergence test P-value:2.2e-47
> test-miss-dep.R: 1
> test-metrics.R: The log-likelihood improved by 1.1346.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: The log-likelihood improved by 1.504.
> test-miss-dep.R: Iteration 2 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1129.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss-dep.R: Convergence test P-value:2.1e-22
> test-miss-dep.R: 1 The log-likelihood improved by 0.5752.
> test-miss-dep.R: Iteration 3 of at most 60:
> test-miss-dep.R: Convergence test P-value:4.7e-04
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.0516.
> test-miss-dep.R: Iteration 4 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss-dep.R: Convergence test P-value:2.1e-03
> test-miss-dep.R: 1
> test-metrics.R: The log-likelihood improved by 0.0037.
> test-miss-dep.R: The log-likelihood improved by 0.03965.
> test-miss-dep.R: Iteration 5 of at most 60:
> test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss-dep.R: Convergence test P-value:2.9e-01
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.004546.
> test-miss-dep.R: Iteration 6 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: Convergence test P-value:7.4e-04
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.0461.
> test-miss-dep.R: Iteration 7 of at most 60:
> test-miss-dep.R: Convergence test P-value:2e-02
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.02163.
> test-miss-dep.R: Iteration 8 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.4e-01
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.008556.
> test-miss-dep.R: Iteration 9 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.4018.
> test-miss-dep.R: Convergence test P-value:9.1e-01
> test-miss-dep.R: Convergence detected. Stopping.
> test-metrics.R: The log-likelihood improved by 3.2780.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by < 0.0001.
> test-miss-dep.R: Finished CD.
> test-miss-dep.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.6020.
> test-metrics.R: The log-likelihood improved by 3.4584.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.2132.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: Post-burnin sample is constant; returning.
> test-miss-dep.R: 1
> test-miss-dep.R: Optimizing with step length 1.0000.
> test-miss-dep.R: The log-likelihood improved by 0.0082.
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0377.
> test-miss-dep.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss-dep.R: Finished MCMLE.
> test-miss-dep.R: Evaluating log-likelihood at the estimate.
> test-miss-dep.R: Setting up bridge sampling...
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss-dep.R: Using 16 bridges:
> test-miss-dep.R: 1
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: 2
> test-miss-dep.R: 3
> test-miss-dep.R: 4
> test-miss-dep.R: 5
> test-miss-dep.R: 6
> test-miss-dep.R: 7
> test-miss-dep.R: 8
> test-miss-dep.R: 9
> test-miss-dep.R: 10
> test-metrics.R: 1 Optimizing with step length 0.4158.
> test-metrics.R: The log-likelihood improved by 1.9115.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss-dep.R: 11
> test-miss-dep.R: 12 13
> test-miss-dep.R: 14
> test-miss-dep.R: 15
> test-miss-dep.R: 16
> test-metrics.R: 1 Optimizing with step length 0.4804.
> test-metrics.R: The log-likelihood improved by 2.5519.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: .
> test-miss-dep.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-miss-dep.R: model likelihood is only implemented for dyad-independent constraints
> test-miss-dep.R: at this time. Number of observations is similarly poorly defined. This
> test-miss-dep.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-miss-dep.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-miss-dep.R: distribution and constraints.
> test-miss-dep.R:
> test-miss-dep.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss-dep.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss.CD.R: n=20, density=0.1, missing=0.1
> test-metrics.R: The log-likelihood improved by 2.9415.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1226.
> test-miss.CD.R: Convergence test P-value:3e-13
> test-miss.CD.R: 1 The log-likelihood improved by 0.2096.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-12
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1974.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:5.8e-11
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.341.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4e-15
> test-miss.CD.R: 1 The log-likelihood improved by 0.1744.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-16
> test-miss.CD.R: 1 The log-likelihood improved by 0.6862.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.3e-15
> test-miss.CD.R: 1 The log-likelihood improved by 0.3025.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-19
> test-miss.CD.R: 1 The log-likelihood improved by 0.1888.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.3e-17
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.2862.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.6e-07
> test-miss.CD.R: 1 The log-likelihood improved by 0.2007.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.1e-02
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.03854.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-05
> test-miss.CD.R: 1 The log-likelihood improved by 0.1259.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1 The log-likelihood improved by 0.0004877.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1 2
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-metrics.R: 3 4 5 6 7 8 9 10 11 Optimizing with step length 0.3934.
> test-metrics.R: The log-likelihood improved by 4.1457.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.5e-68
> test-miss.CD.R: 1 The log-likelihood improved by 0.7099.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.6e-64
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.5925.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.6e-53
> test-miss.CD.R: 1 The log-likelihood improved by 0.6327.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.1e-37
> test-miss.CD.R: 1 The log-likelihood improved by 0.8025.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-30
> test-miss.CD.R: 1 The log-likelihood improved by 0.583.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-19
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6591.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.2e-04
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.05663.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-miss.CD.R: Convergence test P-value:2.4e-01
> test-miss.CD.R: 1
> test-metrics.R: The log-likelihood improved by 2.8651.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 0.007268.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.1e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-54
> test-miss.CD.R: 1 The log-likelihood improved by 0.5854.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.4e-52
> test-miss.CD.R: 1 The log-likelihood improved by 0.6164.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.5e-46
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6486.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-32
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.3360.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: 1 The log-likelihood improved by 1.361.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.8e-14
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.379.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-03
> test-miss.CD.R: 1 The log-likelihood improved by 0.05118.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.9e-02
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.03478.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.5e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.001186.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Network statistics:
> test-miss.CD.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.CD.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.CD.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.CD.R: 0 0 0 0 0 0 0
> test-miss.CD.R: Correct estimate = -2.028148
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0827.
> test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-283
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.712.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-235
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.493.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.3e-184
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.167.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-metrics.R: 1 2 3
> test-metrics.R: 4 5 6 7 8 9 10 11 Optimizing with step length 0.3701.
> test-miss.CD.R: Convergence test P-value:8.9e-150
> test-miss.CD.R: 1 2
> test-metrics.R: The log-likelihood improved by 2.3554.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-147
> test-metrics.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.5218.
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-metrics.R: The log-likelihood improved by 2.9696.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.2e-155
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.8e-153
> test-miss.CD.R: 1 2
> test-metrics.R: 1 2
> test-metrics.R: 3 Optimizing with step length 0.8048.
> test-metrics.R: The log-likelihood improved by 1.8226.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:1e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-153
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.2705.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:8e-150
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.7e-158
> test-miss.CD.R: 1 2
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 13 of at most 60:
> test-metrics.R: The log-likelihood improved by 0.0012.
> test-metrics.R: Convergence test p-value: 0.0099. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:8.6e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 14 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-162
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 15 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 16 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.1e-159
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 17 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2 3
> test-metrics.R: 4 5
> test-metrics.R: 6
> test-metrics.R: 7 8
> test-metrics.R: 9
> test-metrics.R: 10 11
> test-metrics.R: 12 13 Optimizing with step length 0.4397.
> test-metrics.R: The log-likelihood improved by 3.0119.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:3.3e-159
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 18 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.6e-157
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 19 of at most 60:
> test-metrics.R: 1 2 3
> test-miss.CD.R: Convergence test P-value:2.5e-163
> test-miss.CD.R: 1
> test-metrics.R: 4
> test-metrics.R: 5 6
> test-metrics.R: 7 8 9
> test-miss.CD.R: 2
> test-metrics.R: 10 11
> test-metrics.R: 12
> test-metrics.R: Optimizing with step length 0.6225.
> test-metrics.R: The log-likelihood improved by 3.6934.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 20 of at most 60:
> test-miss.CD.R: Convergence test P-value:3.5e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 21 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 1.0488.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.3e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 22 of at most 60:
> test-miss.CD.R: Convergence test P-value:1e-157
> test-miss.CD.R: 1 2
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 23 of at most 60:
> test-metrics.R: The log-likelihood improved by 0.0821.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.4e-160
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 24 of at most 60:
> test-miss.R: n=20, density=0.1, missing=0.05
> test-miss.CD.R: Convergence test P-value:2.1e-158
> test-miss.CD.R: 1 2
> test-miss.R: Correct estimate = -2.118156 with log-likelihood -120.6883 .
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 25 of at most 60:
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.CD.R: Convergence test P-value:1.3e-150
> test-miss.CD.R: 1
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.CD.R: 2
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: MPLE estimate = -2.118156 with log-likelihood -120.6883 OK.
> test-miss.R:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 26 of at most 60:
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.CD.R: Convergence test P-value:4.5e-158
> test-miss.CD.R: 1 2
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 26 dyads is required.
> test-miss.R: Imputing 3 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 41 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 27 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.8e-154
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 28 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.CD.R: Convergence test P-value:8.6e-160
> test-miss.CD.R: 1 2
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 29 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 49.45267
> test-miss.R: Starting MCMLE Optimization...
> test-miss.CD.R: Convergence test P-value:2.9e-157
> test-miss.CD.R: 1 2
> test-miss.R: 1 2 3 4 5 6 7 8 9 10 11 12
> test-miss.R: Optimizing with step length 0.4099.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.5936.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.374066
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 30 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 31 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.9e-155
> test-miss.CD.R: 1 2
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 32 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.CD.R: Convergence test P-value:1.5e-156
> test-miss.CD.R: 1 2
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.36008
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.4981.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.2702.
> test-miss.R: Distance from origin on tolerance region scale: 192.073 (previously 422.077).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.647302
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 33 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.CD.R: Convergence test P-value:1e-153
> test-miss.CD.R: 1 2
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 34 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-165
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 35 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 17.50766
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 Optimizing with step length 0.7736.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.0776.
> test-miss.R: Distance from origin on tolerance region scale: 71.38353 (previously 259.1767).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.95412
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:3.7e-164
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 36 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:3.5e-165
> test-miss.CD.R: 1
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 5.621399
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.CD.R: 2
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.3258.
> test-miss.R: Distance from origin on tolerance region scale: 6.488425 (previously 62.9371).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 5 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.070021
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 37 of at most 60:
> test-miss.CD.R: Convergence test P-value:3.8e-167
> test-miss.CD.R: 1 2
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 32.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 1.853909
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0589.
> test-miss.R: Distance from origin on tolerance region scale: 1.17581 (previously 10.81058).
> test-miss.R: Test statistic: T^2 = 11.54078, with 1 free parameter(s) and 179.1884 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0008. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 38 of at most 60:
> test-miss.R: Dyad-independent submodel MLE has likelihood -120.6883 at:
> test-miss.R: [1] -2.118156 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.CD.R: Convergence test P-value:4.3e-166
> test-miss.CD.R: 1 2
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 39 of at most 60:
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-2.133081, 0.000000].
> test-miss.R: Running theta=[-2.132118, 0.000000].
> test-miss.R: Running theta=[-2.131155, 0.000000].
> test-miss.R: Running theta=[-2.130192, 0.000000].
> test-miss.R: Running theta=[-2.129229, 0.000000].
> test-miss.R: Running theta=[-2.128267, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.8e-166
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 40 of at most 60:
> test-miss.R: Running theta=[-2.127304, 0.000000].
> test-miss.R: Running theta=[-2.126341, 0.000000].
> test-miss.R: Running theta=[-2.125378, 0.000000].
> test-miss.R: Running theta=[-2.124415, 0.000000].
> test-miss.R: Running theta=[-2.123452, 0.000000].
> test-miss.R: Running theta=[-2.122489, 0.000000].
> test-miss.R: Running theta=[-2.121526, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.4e-154
> test-miss.CD.R: 1
> test-miss.R: Running theta=[-2.120563, 0.000000].
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.R: Running theta=[-2.1196, 0.0000].
> test-miss.CD.R: Iteration 41 of at most 60:
> test-miss.R: Running theta=[-2.118637, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.009575496) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.119005, 0.000000].
> test-miss.R: Running theta=[-2.119968, 0.000000].
> test-miss.R: Running theta=[-2.120931, 0.000000].
> test-miss.R: Running theta=[-2.121894, 0.000000].
> test-miss.R: Running theta=[-2.122857, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.3e-139
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.12382, 0.00000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 42 of at most 60:
> test-miss.R: Running theta=[-2.124783, 0.000000].
> test-miss.R: Running theta=[-2.125746, 0.000000].
> test-miss.R: Running theta=[-2.126709, 0.000000].
> test-miss.R: Running theta=[-2.127671, 0.000000].
> test-miss.R: Running theta=[-2.128634, 0.000000].
> test-miss.CD.R: Convergence test P-value:7.6e-160
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.129597, 0.000000].
> test-miss.R: Running theta=[-2.13056, 0.00000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 43 of at most 60:
> test-miss.R: Running theta=[-2.131523, 0.000000].
> test-miss.R: Running theta=[-2.132486, 0.000000].
> test-miss.R: Running theta=[-2.133449, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.007542247) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132854, 0.000000].
> test-miss.R: Running theta=[-2.131891, 0.000000].
> test-miss.CD.R: Convergence test P-value:3e-152
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.130928, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 44 of at most 60:
> test-miss.R: Running theta=[-2.129965, 0.000000].
> test-miss.R: Running theta=[-2.129002, 0.000000].
> test-miss.R: Running theta=[-2.128039, 0.000000].
> test-miss.R: Running theta=[-2.127076, 0.000000].
> test-miss.R: Running theta=[-2.126113, 0.000000].
> test-miss.R: Running theta=[-2.125151, 0.000000].
> test-miss.CD.R: Convergence test P-value:3.1e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.124188, 0.000000].
> test-miss.R: Running theta=[-2.123225, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 45 of at most 60:
> test-miss.R: Running theta=[-2.122262, 0.000000].
> test-miss.R: Running theta=[-2.121299, 0.000000].
> test-miss.R: Running theta=[-2.120336, 0.000000].
> test-miss.R: Running theta=[-2.119373, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.9e-150
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.11841, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.006188692) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.118778, 0.000000].
> test-miss.R: Running theta=[-2.119741, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 46 of at most 60:
> test-miss.R: Running theta=[-2.120704, 0.000000].
> test-miss.R: Running theta=[-2.121667, 0.000000].
> test-miss.R: Running theta=[-2.12263, 0.00000].
> test-miss.R: Running theta=[-2.123593, 0.000000].
> test-miss.R: Running theta=[-2.124555, 0.000000].
> test-miss.CD.R: Convergence test P-value:8.8e-148
> test-miss.CD.R: 1
> test-miss.R: Running theta=[-2.125518, 0.000000].
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 47 of at most 60:
> test-miss.R: Running theta=[-2.126481, 0.000000].
> test-miss.R: Running theta=[-2.127444, 0.000000].
> test-miss.R: Running theta=[-2.128407, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.8e-153
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.12937, 0.00000].
> test-miss.R: Running theta=[-2.130333, 0.000000].
> test-miss.R: Running theta=[-2.131296, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 48 of at most 60:
> test-miss.R: Running theta=[-2.132259, 0.000000].
> test-miss.R: Running theta=[-2.133222, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005396227) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132626, 0.000000].
> test-miss.R: Running theta=[-2.131664, 0.000000].
> test-miss.R: Running theta=[-2.130701, 0.000000].
> test-miss.R: Running theta=[-2.129738, 0.000000].
> test-miss.CD.R: Convergence test P-value:4.8e-158
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.128775, 0.000000].
> test-miss.R: Running theta=[-2.127812, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 49 of at most 60:
> test-miss.R: Running theta=[-2.126849, 0.000000].
> test-miss.R: Running theta=[-2.125886, 0.000000].
> test-miss.R: Running theta=[-2.124923, 0.000000].
> test-miss.R: Running theta=[-2.12396, 0.00000].
> test-miss.R: Running theta=[-2.122997, 0.000000].
> test-miss.R: Running theta=[-2.122035, 0.000000].
> test-miss.CD.R: Convergence test P-value:6e-154
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.121072, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 50 of at most 60:
> test-miss.R: Running theta=[-2.120109, 0.000000].
> test-miss.R: Running theta=[-2.119146, 0.000000].
> test-miss.R: Running theta=[-2.118183, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.4e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 51 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.2e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 52 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.2e-161
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 53 of at most 60:
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1728:32768
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 486
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 1.8539 5.6566 0.2566 0.4463
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -8.881 -1.881 2.119 5.119 14.119
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -2.129e-17 1.663e+00 1.067e-01 1.067e-01
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.8807 -1.3807 0.1193 1.1193 4.0693
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. 1.853909e+00 NA
> test-miss.R: test stat. 4.040502e+00 1.632566e+01
> test-miss.R: P-val. 5.333689e-05 7.904382e-05
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 0.50229634
> test-miss.R: Lag 128 0.27968825
> test-miss.R: Lag 192 0.14989734
> test-miss.R: Lag 256 0.08553620
> test-miss.R: Lag 320 0.01488518
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.04999730
> test-miss.R: Lag 128 0.05488745
> test-miss.R: Lag 192 -0.03080175
> test-miss.R: Lag 256 0.03734623
> test-miss.R: Lag 320 0.01289319
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.4658741
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.6413056
> test-miss.R: Joint P-value (lower = worse): 0.5503774
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.2553269
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.7984706
> test-miss.R: Joint P-value (lower = worse): 0.5503774 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -2.133563 with log-likelihood -120.7039 OK.
> test-miss.CD.R: Convergence test P-value:5.8e-145
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 54 of at most 60:
> test-miss.CD.R: Convergence test P-value:5.8e-163
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 55 of at most 60:
> test-miss.R: Correct estimate = -1.663142 with log-likelihood -79.82064 .
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 56 of at most 60:
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.CD.R: Convergence test P-value:2e-150
> test-miss.CD.R: 1 2
> test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 57 of at most 60:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 8 dyads is required.
> test-miss.R: Imputing 1 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 30 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.4e-151
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 58 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.2e-168
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 59 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:3e-159
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 60 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.15638
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 6 7 8 Optimizing with step length 0.5368.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 4.3000.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.146333
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:6.5e-157
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 14.85185
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 3.2552.
> test-miss.R: Distance from origin on tolerance region scale: 64.83604 (previously 323.1391).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.584689
> test-miss.R: Starting unconstrained MCMC...
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.600823
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1297.
> test-miss.R: Distance from origin on tolerance region scale: 2.582218 (previously 84.20395).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.684393
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.2386831
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0011.
> test-miss.R: Distance from origin on tolerance region scale: 0.02175454 (previously 2.583022).
> test-miss.R: Test statistic: T^2 = 16.95471, with 1 free parameter(s) and 192.1073 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -79.82064 at:
> test-miss.R: [1] -1.663142 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-1.674863, 0.000000].
> test-miss.R: Running theta=[-1.674107, 0.000000].
> test-miss.R: Running theta=[-1.673351, 0.000000].
> test-miss.R: Running theta=[-1.672594, 0.000000].
> test-miss.R: Running theta=[-1.671838, 0.000000].
> test-miss.R: Running theta=[-1.671082, 0.000000].
> test-miss.R: Running theta=[-1.670326, 0.000000].
> test-miss.R: Running theta=[-1.66957, 0.00000].
> test-miss.R: Running theta=[-1.668813, 0.000000].
> test-miss.R: Running theta=[-1.668057, 0.000000].
> test-miss.R: Running theta=[-1.667301, 0.000000].
> test-miss.R: Running theta=[-1.666545, 0.000000].
> test-miss.R: Running theta=[-1.665789, 0.000000].
> test-miss.R: Running theta=[-1.665033, 0.000000].
> test-miss.R: Running theta=[-1.664276, 0.000000].
> test-miss.R: Running theta=[-1.66352, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005483681) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-1.663809, 0.000000].
> test-miss.R: Running theta=[-1.664565, 0.000000].
> test-miss.R: Running theta=[-1.665321, 0.000000].
> test-miss.R: Running theta=[-1.666078, 0.000000].
> test-miss.R: Running theta=[-1.666834, 0.000000].
> test-miss.R: Running theta=[-1.66759, 0.00000].
> test-miss.R: Running theta=[-1.668346, 0.000000].
> test-miss.R: Running theta=[-1.669102, 0.000000].
> test-miss.R: Running theta=[-1.669858, 0.000000].
> test-miss.R: Running theta=[-1.670615, 0.000000].
> test-miss.R: Running theta=[-1.671371, 0.000000].
> test-miss.R: Running theta=[-1.672127, 0.000000].
> test-miss.R: Running theta=[-1.672883, 0.000000].
> test-miss.R: Running theta=[-1.673639, 0.000000].
> test-miss.R: Running theta=[-1.674396, 0.000000].
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: Running theta=[-1.675152, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.2387 5.2156 0.3346 0.3867
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -9.2346 -4.2346 -0.2346 2.7654 11.7154
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 4.040e-17 1.007e+00 6.463e-02 6.463e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -1.2346 -0.7346 -0.2346 0.7654 2.7154
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.2386831 NA
> test-miss.R: test stat. -0.6087923 0.3706280
> test-miss.R: P-val. 0.5426621 0.5433813
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.000000000
> test-miss.R: Lag 128 0.141730657
> test-miss.R: Lag 256 -0.008044799
> test-miss.R: Lag 384 0.039503814
> test-miss.R: Lag 512 0.016265240
> test-miss.R: Lag 640 0.025525909
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.03837238
> test-miss.R: Lag 128 0.06102163
> test-miss.R: Lag 192 0.01817600
> test-miss.R: Lag 256 -0.07663989
> test-miss.R: Lag 320 -0.02107378
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -1.387683
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.1652336
> test-miss.R: Joint P-value (lower = worse): 0.2706448
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.5702328
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.5685198
> test-miss.R: Joint P-value (lower = worse): 0.2706448 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -1.675241 with log-likelihood -79.81794 OK.
> test-miss.R: Correct estimate = -3.157 with log-likelihood -8.355963 .
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: MPLE estimate = -3.157 with log-likelihood -8.355963 OK.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal: 'ergm:MH_TNT'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 2 dyads is required.
> test-miss.R: Imputing 0 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 2 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.942387
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.8416.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.729057
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 0.9012346
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1847.
> test-miss.R: Distance from origin on tolerance region scale: 3.678483 (previously 39.20962).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -3.138904
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.05349794
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0008.
> test-miss.R: Distance from origin on tolerance region scale: 0.01512904 (previously 4.293514).
> test-miss.R: Test statistic: T^2 = 22.84874, with 1 free parameter(s) and 256.603 degrees of freedom.
> test-miss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -8.355963 at:
> test-miss.R: [1] -3.157 0.000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-3.11196, 0.00000].
> test-miss.R: Running theta=[-3.114866, 0.000000].
> test-miss.R: Running theta=[-3.117772, 0.000000].
> test-miss.R: Running theta=[-3.120678, 0.000000].
> test-miss.R: Running theta=[-3.123584, 0.000000].
> test-miss.R: Running theta=[-3.126489, 0.000000].
> test-miss.R: Running theta=[-3.129395, 0.000000].
> test-miss.R: Running theta=[-3.132301, 0.000000].
> test-miss.R: Running theta=[-3.135207, 0.000000].
> test-miss.R: Running theta=[-3.138113, 0.000000].
> test-miss.R: Running theta=[-3.141018, 0.000000].
> test-miss.R: Running theta=[-3.143924, 0.000000].
> test-miss.R: Running theta=[-3.14683, 0.00000].
> test-miss.R: Running theta=[-3.149736, 0.000000].
> test-miss.R: Running theta=[-3.152642, 0.000000].
> test-miss.R: Running theta=[-3.155548, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 3584:65536
> test-miss.R: Thinning interval = 256
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.05350 1.39509 0.08949 0.08949
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.05761 -1.05761 -0.05761 0.94239 2.94239
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 9.838e-19 2.335e-01 1.498e-02 1.763e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -0.05761 -0.05761 -0.05761 -0.05761 0.94239
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.05349794 NA
> test-miss.R: test stat. -0.58650248 0.3476007
> test-miss.R: P-val. 0.55753790 0.5559932
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 256 -0.07640761
> test-miss.R: Lag 512 0.04674441
> test-miss.R: Lag 768 -0.08703223
> test-miss.R: Lag 1024 -0.01273911
> test-miss.R: Lag 1280 0.08918131
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 128 0.01440843
> test-miss.R: Lag 256 -0.06163854
> test-miss.R: Lag 384 -0.05752332
> test-miss.R: Lag 512 0.09381586
> test-miss.R: Lag 640 0.16935966
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 1.222791
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.2214088
> test-miss.R: Joint P-value (lower = worse): 0.530107
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 0.899493
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.3683901
> test-miss.R: Joint P-value (lower = worse): 0.530107 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -3.110507 with log-likelihood -8.357166 OK.
> test-miss.R: Network statistics:
> test-miss.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.R: 0 0 0 0 0 0 0
> test-miss.R: Correct estimate = -2.028148
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Iteration 1 of at most 5:
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6
> test-miss.R: 7 8
> test-miss.R: 9
> test-miss.R: 10 11 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 2 of at most 5:
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: 1 2 3 4
> test-miss.R: 5 6 7 8 9 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 3 of at most 5:
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6
> test-miss.R: 7
> test-miss.R: 8
> test-miss.R: 9 10 11 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 4 of at most 5:
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: 1 2
> test-miss.R: 3 4 5 6
> test-miss.R: 7
> test-miss.R: 8
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 5 of at most 5:
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6 7 8 9 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
> test-miss.R: MCMLE estimation did not converge after 5 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Using 16 bridges: 1
> test-miss.R: 2
> test-miss.R: 3
> test-miss.R: 4
> test-miss.R: 5
> test-miss.R: 6
> test-miss.R: 7 8
> test-miss.R: 9
> test-miss.R: 10
> test-miss.R: 11
> test-miss.R: 12
> test-miss.R: 13
> test-miss.R: 14
> test-miss.R: 15 16
> test-miss.R: .
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-offset.R: Obtaining the responsible dyads.
> test-mple-offset.R: Evaluating the predictor and response matrix.
> test-mple-offset.R: Maximizing the pseudolikelihood.
> test-mple-offset.R: Finished MPLE.
> test-mple-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-offset.R: Iteration 1 of at most 60:
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-offset.R: 1 Optimizing with step length 1.0000.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-offset.R: The log-likelihood improved by 0.0040.
> test-mple-offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-mple-offset.R: Finished MCMLE.
> test-mple-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-mple-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-target.R: [1] 350 50 250
> test-mple-target.R: Structural check:
> test-mple-target.R: Mean degree: 1.4 .
> test-mple-target.R: Average degree among nodes with degree 2 or higher: 2.25 .
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-target.R: Iteration 1 of at most 60:
> test-networkLite.R: Loading required package: networkLite
> test-mple-cov.R: Estimating Bootstrap Standard Errors using 500 simulated networks.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4 5 6 7
> test-networkLite.R: 8 9
> test-networkLite.R: 10 11 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 2
> test-networkLite.R: 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4 5 6 7 8 9 10 11 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4 5 6 7 8 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2
> test-networkLite.R: 3
> test-networkLite.R: 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nodrop.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-nodrop.R: The log-likelihood improved by 0.0005.
> test-nodrop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-nodrop.R: Finished MCMLE.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 1 2
> test-networkLite.R: 3 4 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8 9 10 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-nodrop.R: 1 Optimizing with step length 0.2247.
> test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: The log-likelihood improved by 2.2822.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-nodrop.R: 1 Optimizing with step length 0.2817.
> test-nodrop.R: The log-likelihood improved by 2.4734.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 1 Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.2675.
> test-nodrop.R: The log-likelihood improved by 3.3752.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-nodrop.R: 1 Optimizing with step length 0.3098.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-nodrop.R: The log-likelihood improved by 2.4783.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-nonident-test.R: 1 Optimizing with step length 1.0000.
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
> test-networkLite.R: 21
> test-networkLite.R: 22
> test-networkLite.R: 23 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nonident-test.R: 1 Optimizing with step length 0.8147.
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonunique-names.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonunique-names.R: Obtaining the responsible dyads.
> test-nonunique-names.R: Evaluating the predictor and response matrix.
> test-nonunique-names.R: Maximizing the pseudolikelihood.
> test-nonunique-names.R: Finished MPLE.
> test-nonunique-names.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonunique-names.R: Iteration 1 of at most 1:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-nonunique-names.R: 1 Optimizing with step length 1.0000.
> test-nonunique-names.R: The log-likelihood improved by 0.0084.
> test-nonunique-names.R: Convergence test p-value: 0.0480.
> test-nonunique-names.R: Not converged with 99% confidence; increasing sample size.
> test-nonunique-names.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonunique-names.R: Finished MCMLE.
> test-nonunique-names.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonunique-names.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonunique-names.R: Sample statistics summary:
> test-nonunique-names.R:
> test-nonunique-names.R: Iterations = 2304:44032
> test-nonunique-names.R: Thinning interval = 128
> test-nonunique-names.R: Number of chains = 1
> test-nonunique-names.R: Sample size per chain = 327
> test-nonunique-names.R:
> test-nonunique-names.R: 1. Empirical mean and standard deviation for each variable,
> test-nonunique-names.R: plus standard error of the mean:
> test-nonunique-names.R:
> test-nonunique-names.R: Mean SD Naive SE Time-series SE
> test-nonunique-names.R: edgecov.a -0.2171 3.380 0.1869 0.3114
> test-nonunique-names.R: edgecov.a 0.1346 3.565 0.1971 0.4311
> test-nonunique-names.R:
> test-nonunique-names.R: 2. Quantiles for each variable:
> test-nonunique-names.R:
> test-nonunique-names.R: 2.5% 25% 50% 75% 97.5%
> test-nonunique-names.R: edgecov.a -7 -2 0 2 6.85
> test-nonunique-names.R: edgecov.a -7 -2 0 3 7.00
> test-nonunique-names.R:
> test-nonunique-names.R:
> test-nonunique-names.R: Are sample statistics significantly different from observed?
> test-nonunique-names.R:
> test-nonunique-names.R: edgecov.a edgecov.a (Omni)
> test-nonunique-names.R: diff. -0.2171254 0.1345566 NA
> test-nonunique-names.R: test stat. -0.6971750 0.3120903 1.2954084
> test-nonunique-names.R: P-val. 0.4856933 0.7549719 0.5307488
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics cross-correlations:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: edgecov.a 1.0000000 0.6853513
> test-nonunique-names.R: edgecov.a 0.6853513 1.0000000
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics auto-correlation:
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: Lag 0 1.000000000 1.00000000
> test-nonunique-names.R: Lag 128 0.592467538 0.65334235
> test-nonunique-names.R: Lag 256 0.298337375 0.41906307
> test-nonunique-names.R: Lag 384 0.082532656 0.22830518
> test-nonunique-names.R: Lag 512 -0.003477022 0.08811653
> test-nonunique-names.R: Lag 640 -0.090771181 0.03701357
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics burn-in diagnostic (Geweke):
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R:
> test-nonunique-names.R: Fraction in 1st window = 0.1
> test-nonunique-names.R: Fraction in 2nd window = 0.5
> test-nonunique-names.R:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: -0.41748721 0.04619135
> test-nonunique-names.R:
> test-nonunique-names.R: Individual P-values (lower = worse):
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: 0.6763221 0.9631577
> test-nonunique-names.R: Joint P-value (lower = worse): 0.8447246
> test-nonunique-names.R:
> test-nonunique-names.R: Note: MCMC diagnostics shown here are from the last round of
> test-nonunique-names.R: simulation, prior to computation of final parameter estimates.
> test-nonunique-names.R: Because the final estimates are refinements of those used for this
> test-nonunique-names.R: simulation run, these diagnostics may understate model performance.
> test-nonunique-names.R: To directly assess the performance of the final model on in-model
> test-nonunique-names.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-nonunique-names.R: GOF=~model).
> test-nonunique-names.R:
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: 1
> test-networkLite.R: 2
> test-networkLite.R: 3
> test-networkLite.R: 4
> test-networkLite.R: 5
> test-networkLite.R: 6
> test-networkLite.R: 7
> test-networkLite.R: 8 9 10 11 12 13 14 15
> test-networkLite.R: 16 17
> test-networkLite.R: 18 19 20 21 22
> test-networkLite.R: 23 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: 1 2 3 4 5 6 7 8 9 10 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: Convergence test p-value: 0.0010.
> test-networkLite.R: Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.6004.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.0061.
> test-offsets.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: Setting up bridge sampling...
> test-offsets.R: Using 16 bridges: 1
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: 2
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: 3
> test-offsets.R: 4
> test-offsets.R: 5
> test-offsets.R: 6 7
> test-offsets.R: 8
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-offsets.R: 9 10
> test-offsets.R: 11
> test-offsets.R: 12
> test-offsets.R: 13
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: 14 15
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-offsets.R: 16
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.7959.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-parallel.R:
> test-parallel.R: Attaching package: 'statnet.common'
> test-parallel.R:
> test-parallel.R: The following objects are masked from 'package:base':
> test-parallel.R:
> test-parallel.R: attr, order, replace
> test-parallel.R:
> test-parallel.R: parallel test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run.
> test-parallel.R: Skipping OpenMP test. This package installation was built without OpenMP support.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 1 Optimizing with step length 1.0000.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: The log-likelihood improved by 0.0207.
> test-offsets.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-offsets.R: Using 16 bridges: 1
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 2
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 3
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 4
> test-offsets.R: 5
> test-offsets.R: 6
> test-offsets.R: 7
> test-offsets.R: 8
> test-offsets.R: 9
> test-offsets.R: 10
> test-offsets.R: 11
> test-offsets.R: 12
> test-offsets.R: 13
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 14
> test-offsets.R: 15
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 16
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-runtime-diags.R: Obtaining the responsible dyads.
> test-runtime-diags.R: Evaluating the predictor and response matrix.
> test-runtime-diags.R: Maximizing the pseudolikelihood.
> test-runtime-diags.R: Finished MPLE.
> test-runtime-diags.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-runtime-diags.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: 1 Optimizing with step length 1.0000.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-runtime-diags.R: The log-likelihood improved by 0.0414.
> test-runtime-diags.R: Convergence test p-value: 0.0016. Converged with 99% confidence.
> test-runtime-diags.R: Finished MCMLE.
> test-runtime-diags.R: This model was fit using MCMC. To examine model diagnostics and check
> test-runtime-diags.R: for degeneracy, use the mcmc.diagnostics() function.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-scoping.R: 1 Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate.
> test-scoping.R: Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges: 1 2 3
> test-scoping.R: 4
> test-scoping.R: 5 6 7 8 9
> test-scoping.R: 10 11
> test-scoping.R: 12
> test-scoping.R: 13
> test-scoping.R: 14
> test-scoping.R: 15 16 .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-scoping.R: 1
> test-scoping.R: Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges: 1
> test-scoping.R: 2
> test-scoping.R: 3
> test-scoping.R: 4 5 6 7
> test-scoping.R: 8 9
> test-scoping.R: 10
> test-scoping.R: 11
> test-scoping.R: 12
> test-scoping.R: 13 14 15
> test-scoping.R: 16 .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-shrink-into-CH.R: 1 2 3 4
> test-shrink-into-CH.R: 5
> test-shrink-into-CH.R: 6
> test-shrink-into-CH.R: 7 8 9 10
> test-shrink-into-CH.R: 11 12 13 14 15
> test-shrink-into-CH.R: 16
> test-shrink-into-CH.R: 17
> test-shrink-into-CH.R: 18 19 20
> test-shrink-into-CH.R: 1
> test-shrink-into-CH.R: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
> test-shrink-into-CH.R: 16
> test-shrink-into-CH.R: 17
> test-shrink-into-CH.R: 18 19 20
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R:
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R:
> test-skip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-skip.R: Iteration 1 of at most 60:
> test-skip.R: 1 Optimizing with step length 1.0000.
> test-skip.R: The log-likelihood improved by 0.0360.
> test-skip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-skip.R: Finished MCMLE.
> test-skip.R: This model was fit using MCMC. To examine model diagnostics and check
> test-skip.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-snctrl.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-snctrl.R: Obtaining the responsible dyads.
> test-snctrl.R: Evaluating the predictor and response matrix.
> test-snctrl.R: Maximizing the pseudolikelihood.
> test-snctrl.R: Finished MPLE.
> test-snctrl.R: Evaluating log-likelihood at the estimate.
> test-snctrl.R:
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.7009355 0.2208488
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.6617183 0.1405334
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1 Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0034.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.5333754 -0.1317716 0.6729982
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.57231795 -0.05712682 0.44962020
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1 Optimizing with step length 1.0000.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: The log-likelihood improved by 0.0007.
> test-stocapprox.R: Convergence test p-value: 0.0001.
> test-stocapprox.R: Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.1e-111
> test-stocapprox.R: 1 The log-likelihood improved by 1.945.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-44
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.5962.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:4e-07
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.06364.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.6e-05
> test-stocapprox.R: 1 The log-likelihood improved by 0.04097.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.3e-03
> test-stocapprox.R: 1 The log-likelihood improved by 0.01842.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.743217 0.112619
> test-stocapprox.R: The log-likelihood improved by 0.002083.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.7631980 0.1383531
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-98
> test-stocapprox.R: 1 The log-likelihood improved by 1.862.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.5e-30
> test-stocapprox.R: 1 The log-likelihood improved by 0.3427.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:3e-09
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.08204.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.9e-02
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.01313.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.2e-02
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.009411.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:2.6e-01
> test-stocapprox.R: 1 The log-likelihood improved by 0.005336.
> test-stocapprox.R: Iteration 7 of at most 60:
> test-stocapprox.R: Convergence test P-value:7.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.0009177.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0022.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R:
> test-stocapprox.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-stocapprox.R: * 'news(package="ergm.count")' for changes since last version
> test-stocapprox.R: * 'citation("ergm.count")' for citation information
> test-stocapprox.R: * 'https://statnet.org' for help, support, and other information
> test-stocapprox.R:
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 60:
> test-target-offset.R: 1 Optimizing with step length 1.0000.
> test-target-offset.R: The log-likelihood improved by 0.0210.
> test-target-offset.R: Convergence test p-value: 0.0002. Converged with 99% confidence.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-target-offset.R: Using 16 bridges:
> test-target-offset.R: 1 2 3
> test-target-offset.R: 4 5
> test-target-offset.R: 6
> test-target-offset.R: 7 8
> test-target-offset.R: 9
> test-target-offset.R: 10
> test-target-offset.R: 11
> test-target-offset.R: 12 13
> test-target-offset.R: 14
> test-target-offset.R: 15
> test-target-offset.R: 16
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-target-offset.R: Sample statistics summary:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R:
> test-target-offset.R: Iterations = 14336:262144
> test-target-offset.R: Thinning interval = 1024
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 0.55556 4.490 0.2880 0.2880
> test-target-offset.R: degree1 0.04527 2.005 0.1286 0.1286
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges -7 -2.5 0 3 9.00
> test-target-offset.R: degree1 -3 -1.0 0 1 4.95
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Are sample statistics significantly different from observed?
> test-target-offset.R: edges degree1 (Omni)
> test-target-offset.R: diff. 0.55555556 0.04526749 NA
> test-target-offset.R: test stat. 1.92893422 0.35200754 9.455405747
> test-target-offset.R: P-val. 0.05373903 0.72483261 0.009922641
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges degree1
> test-target-offset.R: edges 1.0000000 -0.7250142
> test-target-offset.R: degree1 -0.7250142 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges degree1
> test-target-offset.R: Lag 0 1.000000000 1.000000000
> test-target-offset.R: Lag 1024 -0.061427219 0.030194492
> test-target-offset.R: Lag 2048 0.007868535 0.092075103
> test-target-offset.R: Lag 3072 0.018624816 0.047810603
> test-target-offset.R: Lag 4096 -0.047471894 0.007659206
> test-target-offset.R: Lag 5120 -0.073593205 -0.009870131
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-target-offset.R: Chain 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.4498910 -0.1601093
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.652789 0.872795
> test-target-offset.R: Joint P-value (lower = worse): 0.8380775
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-target-offset.R: Obtaining the responsible dyads.
> test-target-offset.R: Evaluating the predictor and response matrix.
> test-target-offset.R: Maximizing the pseudolikelihood.
> test-target-offset.R: Finished MPLE.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.7297.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 2 of at most 3:
> test-target-offset.R: 1 Optimizing with step length 0.6389.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 3 of at most 3:
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.8386.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: MCMLE estimation did not converge after 3 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-target-offset.R: Using 16 bridges: 1
> test-target-offset.R: 2 3 4 5 6
> test-target-offset.R: 7 8 9
> test-target-offset.R: 10
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 11
> test-target-offset.R: 12 13
> test-target-offset.R: 14
> test-target-offset.R: 15 16
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 3584:65536
> test-target-offset.R: Thinning interval = 256
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 12.77778 5.118432 0.32835 0.3283476
> test-target-offset.R: gwdegree 0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R: gwdegree.decay 0.01236 0.002961 0.00019 0.0001293
> test-target-offset.R: degree0 -0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges 3.000e+00 9.00000 13.00000 16.00000 23.00000
> test-target-offset.R: gwdegree 1.063e-12 1.00000 1.00000 1.00000 1.00000
> test-target-offset.R: gwdegree.decay 5.955e-03 0.01191 0.01191 0.01489 0.01489
> test-target-offset.R: degree0 -1.000e+00 -1.00000 -1.00000 -1.00000 0.00000
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: edges 1.0000000 0.2709648 0.6049364 -0.2709648
> test-target-offset.R: gwdegree 0.2709648 1.0000000 0.5143643 -1.0000000
> test-target-offset.R: gwdegree.decay 0.6049364 0.5143643 1.0000000 -0.5143643
> test-target-offset.R: degree0 -0.2709648 -1.0000000 -0.5143643 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: Lag 0 1.000000000 1.00000000 1.000000000 1.00000000
> test-target-offset.R: Lag 256 0.006056003 0.02865300 -0.034893817 0.02865300
> test-target-offset.R: Lag 512 0.062813023 -0.07862186 0.002608612 -0.07862186
> test-target-offset.R: Lag 768 -0.089332866 -0.07930006 -0.150790861 -0.07930006
> test-target-offset.R: Lag 1024 -0.091496281 -0.07564135 -0.189123113 -0.07564135
> test-target-offset.R: Lag 1280 0.030192390 0.03461402 0.073975025 0.03461402
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Chain
> test-target-offset.R: 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.05663036 -1.34419649 0.38772965 1.34419649
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.9548397 0.1788849 0.6982161 0.1788849
> test-target-offset.R: Joint P-value (lower = worse): 0.3850888
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: 1 Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0066.
> test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-term-Offset.R: 1 Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0024.
> test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
Saving _problems/test-term-Offset-21.R
Saving _problems/test-term-Offset-25.R
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-b12nodematch.R: In term 'b1nodematch' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b1dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1factor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1twostar' in package 'ergm': Argument 'base' has been superseded by 'levels2', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: In term 'asymmetric' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b2dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) ideg7+.homophily.group and ideg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) gwodeg.fixed.0 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: In term 'nodeifactor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) odeg7+ and odeg8+ are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Observed statistic(s) odeg6+.homophily.group, odeg7+.homophily.group, and odeg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Observed statistic(s) edgecov.YearsTrusted are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-flexible.R: All terms are either offsets or extreme values. No optimization is performed.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-mm.R: Note: Term 'mm(~Grade >= 10, levels = -1)' skipped because it contributes no statistics.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: 1 Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0013.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Fitting the dyad-independent submodel...
> test-term-options.R: Bridging between the dyad-independent submodel and the full model...
> test-term-options.R: Setting up bridge sampling...
> test-term-options.R: Using 16 bridges: 1
> test-term-options.R: 2
> test-term-options.R: 3
> test-term-options.R: 4
> test-term-options.R: 5 6 7
> test-term-options.R: 8
> test-term-options.R: 9
> test-term-options.R: 10
> test-term-options.R: 11 12
> test-term-options.R: 13
> test-term-options.R: 14
> test-term-options.R: 15
> test-term-options.R: 16
> test-term-options.R: .
> test-term-options.R: Bridging finished.
> test-term-options.R:
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-options.R: 1
> test-term-options.R: Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0003.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'DiscUnif2' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-u-function.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'.
> test-valued-sim.R: mean=1, var=4, corr=0.3
> test-valued-sim.R: eta=(0.192307692307692,0.0824175824175824,0.362637362637363)
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated mean (stats only):0.9964445
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated means (target=1):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 1.0034478 0.9683903
> test-valued-sim.R: [2,] 1.0302079 NA 0.8923730
> test-valued-sim.R: [3,] 0.6870641 0.7628159 NA
> test-valued-sim.R: Simulated vars (target=4):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 3.863045 4.034701
> test-valued-sim.R: [2,] 3.944388 NA 3.888997
> test-valued-sim.R: [3,] 3.740890 4.202553 NA
> test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3):
> test-valued-sim.R: [1] 0.2781206 0.2324470 0.3420405
> test-valued-sim.R: ==== output='stats', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R:
> test-valued-sim.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-sim.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-sim.R: * 'citation("ergm.count")' for citation information
> test-valued-sim.R: * 'https://statnet.org' for help, support, and other information
> test-valued-sim.R:
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R:
> test-valued-terms.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-terms.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-terms.R: * 'citation("ergm.count")' for citation information
> test-valued-terms.R: * 'https://statnet.org' for help, support, and other information
> test-valued-terms.R:
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• empty test (1):
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-term-Offset.R:19:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `off <- ergm(nw ~ edges + offset(triangle), offset.coef = 0.1)` to throw a message.
── Failure ('test-term-Offset.R:23:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `Off <- ergm(nw ~ edges + Offset(~triangle, which = 1, coef = 0.1))` to throw a message.
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
Error:
! Test failures.
Execution halted
Flavor: r-patched-linux-x86_64
Version: 4.10.1
Flags: --no-vignettes
Check: tests
Result: ERROR
Running ‘requireNamespaceTest.R’ [3s/4s]
Running ‘testthat.R’ [348s/189s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # File tests/testthat.R in package ergm, part of the Statnet suite of packages
> # for network analysis, https://statnet.org .
> #
> # This software is distributed under the GPL-3 license. It is free, open
> # source, and has the attribution requirements (GPL Section 7) at
> # https://statnet.org/attribution .
> #
> # Copyright 2003-2025 Statnet Commons
> ################################################################################
> library(testthat)
> library(ergm)
Loading required package: network
'network' 1.19.0 (2024-12-08), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4.10.1 (2025-08-26), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.
>
> test_check("ergm")
Starting 2 test processes.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges:
> test-basis.R: 1 2
> test-basis.R: 3
> test-basis.R: 4 5
> test-basis.R: 6
> test-basis.R: 7 8 9
> test-basis.R: 10 11 12
> test-basis.R: 13
> test-basis.R: 14
> test-basis.R: 15 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011.
> test-basis.R: Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges: 1 2
> test-basis.R: 3 4 5
> test-basis.R: 6 7
> test-basis.R: 8
> test-basis.R: 9
> test-basis.R: 10
> test-basis.R: 11 12 13
> test-basis.R: 14 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-basis.R: Obtaining the responsible dyads.
> test-basis.R: Evaluating the predictor and response matrix.
> test-basis.R: Maximizing the pseudolikelihood.
> test-basis.R: Finished MPLE.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.6124.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0128.
> test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-basis.R: Bridging between the dyad-independent submodel and the full model...
> test-basis.R: Setting up bridge sampling...
> test-basis.R: Using 16 bridges:
> test-basis.R: 1 2 3
> test-basis.R: 4
> test-basis.R: 5
> test-basis.R: 6 7
> test-basis.R: 8 9
> test-basis.R: 10
> test-basis.R: 11
> test-basis.R: 12
> test-basis.R: 13
> test-basis.R: 14 15
> test-basis.R: 16
> test-basis.R: .
> test-basis.R: Bridging finished.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1 The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1 The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: Attaching package: 'statnet.common'
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: The following objects are masked from 'package:base':
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: attr, order, replace
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-bridge-target.stats.R: Unable to match target stats. Using MCMLE estimation.
> test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-bridge-target.stats.R: Obtaining the responsible dyads.
> test-bridge-target.stats.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: Maximizing the pseudolikelihood.
> test-bridge-target.stats.R: Finished MPLE.
> test-bridge-target.stats.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-bridge-target.stats.R: Iteration 1 of at most 60:
> test-basis.R: 1 Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-bridge-target.stats.R: 1
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1
> test-bridge-target.stats.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: The log-likelihood improved by 0.0219.
> test-bridge-target.stats.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-bridge-target.stats.R: Finished MCMLE.
> test-bridge-target.stats.R: Evaluating log-likelihood at the estimate.
> test-basis.R: 2
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-basis.R: 3
> test-basis.R: 4 5
> test-basis.R: 6
> test-basis.R: 7
> test-basis.R: 8
> test-basis.R: 9 10
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-basis.R: 11
> test-basis.R: 12 13
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-basis.R: 14 15
> test-bridge-target.stats.R: 2 3
> test-basis.R: 16 .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 4 5 6
> test-bridge-target.stats.R: 7
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 8
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-bridge-target.stats.R: 9 10
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 11
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-bridge-target.stats.R: 12
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1
> test-bridge-target.stats.R: 13
> test-basis.R: The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-bridge-target.stats.R: 14
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-bridge-target.stats.R: 15
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1 The log-likelihood improved by 0.0608.
> test-bridge-target.stats.R: 16
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R:
> test-bridge-target.stats.R: This model was fit using MCMC. To examine model diagnostics and check
> test-bridge-target.stats.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 6
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-bridge-target.stats.R: 7
> test-basis.R: 3
> test-basis.R: 4 5
> test-basis.R: 6 7 8
> test-basis.R: 9
> test-basis.R: 10
> test-bridge-target.stats.R: 8
> test-basis.R: 11 12
> test-basis.R: 13
> test-basis.R: 14 15 16
> test-bridge-target.stats.R: 9
> test-basis.R: .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-bridge-target.stats.R: 10
> test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-basis.R: Iteration 1 of at most 60:
> test-basis.R: Convergence test P-value:4.7e-173
> test-basis.R: 1 The log-likelihood improved by 1.861.
> test-basis.R: Iteration 2 of at most 60:
> test-basis.R: Convergence test P-value:2.5e-135
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 1.577.
> test-basis.R: Iteration 3 of at most 60:
> test-basis.R: Convergence test P-value:1.9e-80
> test-basis.R: 1 The log-likelihood improved by 0.8158.
> test-basis.R: Iteration 4 of at most 60:
> test-bridge-target.stats.R: 11
> test-basis.R: Convergence test P-value:4.5e-32
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.2411.
> test-basis.R: Iteration 5 of at most 60:
> test-basis.R: Convergence test P-value:1.3e-09
> test-basis.R: 1
> test-basis.R: The log-likelihood improved by 0.0608.
> test-basis.R: Iteration 6 of at most 60:
> test-basis.R: Convergence test P-value:2.8e-03
> test-basis.R: 1 The log-likelihood improved by 0.01871.
> test-basis.R: Iteration 7 of at most 60:
> test-basis.R: Convergence test P-value:7.5e-01
> test-basis.R: Convergence detected. Stopping.
> test-basis.R: 1 The log-likelihood improved by 0.001578.
> test-basis.R: Finished CD.
> test-bridge-target.stats.R: 12
> test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-basis.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.1892.
> test-basis.R: Estimating equations are not within tolerance region.
> test-basis.R: Iteration 2 of at most 60:
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-basis.R: 1
> test-basis.R: Optimizing with step length 1.0000.
> test-basis.R: The log-likelihood improved by 0.0072.
> test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-basis.R: Finished MCMLE.
> test-basis.R: Evaluating log-likelihood at the estimate.
> test-basis.R: Setting up bridge sampling...
> test-bridge-target.stats.R: 6
> test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-basis.R: Using 16 bridges: 1
> test-basis.R: 2
> test-basis.R: 3
> test-basis.R: 4
> test-basis.R: 5
> test-bridge-target.stats.R: 7
> test-basis.R: 6
> test-basis.R: 7 8 9 10 11
> test-basis.R: 12
> test-basis.R: 13
> test-bridge-target.stats.R: 8
> test-basis.R: 14
> test-basis.R: 15
> test-basis.R: 16 .
> test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-basis.R: model likelihood is only implemented for dyad-independent constraints
> test-basis.R: at this time. Number of observations is similarly poorly defined. This
> test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-basis.R: distribution and constraints.
> test-basis.R:
> test-basis.R: This model was fit using MCMC. To examine model diagnostics and check
> test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-bridge-target.stats.R: 14
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Iteration 1 of at most 60:
> test-checkpointing.R: Saving state in '/home/hornik/tmp/scratch/Rtmp4Gcw9R/file3de55055c43bfb_001.RData'.
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-checkpointing.R: The log-likelihood improved by 0.0213.
> test-checkpointing.R: Step length converged once. Increasing MCMC sample size.
> test-checkpointing.R: Iteration 2 of at most 60:
> test-checkpointing.R: Saving state in '/home/hornik/tmp/scratch/Rtmp4Gcw9R/file3de55055c43bfb_002.RData'.
> test-bridge-target.stats.R: 3
> test-bridge-target.stats.R: 4
> test-bridge-target.stats.R: 5
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-checkpointing.R: 1
> test-bridge-target.stats.R: 9
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-checkpointing.R: The log-likelihood improved by 0.0238.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: 10
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-bridge-target.stats.R: 11
> test-checkpointing.R: Using 16 bridges: 1 2
> test-bridge-target.stats.R: 12
> test-checkpointing.R: 3
> test-checkpointing.R: 4 5 6
> test-bridge-target.stats.R: 13
> test-checkpointing.R: 7
> test-checkpointing.R: 8
> test-checkpointing.R: 9
> test-bridge-target.stats.R: 14
> test-checkpointing.R: 10 11
> test-checkpointing.R: 12
> test-checkpointing.R: 13 14
> test-checkpointing.R: 15
> test-checkpointing.R: 16
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-checkpointing.R: Obtaining the responsible dyads.
> test-checkpointing.R: Evaluating the predictor and response matrix.
> test-checkpointing.R: Maximizing the pseudolikelihood.
> test-checkpointing.R: Finished MPLE.
> test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-checkpointing.R: Resuming from state saved in '/home/hornik/tmp/scratch/Rtmp4Gcw9R/file3de55055c43bfb_002.RData'.
> test-checkpointing.R: Iteration 1 of at most 60:
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-bridge-target.stats.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
> test-bridge-target.stats.R: Setting up bridge sampling...
> test-bridge-target.stats.R: Using 16 bridges: 1
> test-bridge-target.stats.R: 2
> test-checkpointing.R: 1
> test-checkpointing.R: Optimizing with step length 1.0000.
> test-bridge-target.stats.R: 3
> test-checkpointing.R: The log-likelihood improved by 0.0145.
> test-checkpointing.R: Step length converged twice. Stopping.
> test-checkpointing.R: Finished MCMLE.
> test-checkpointing.R: Evaluating log-likelihood at the estimate.
> test-checkpointing.R: Fitting the dyad-independent submodel...
> test-bridge-target.stats.R: 4
> test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
> test-checkpointing.R: Setting up bridge sampling...
> test-checkpointing.R: Using 16 bridges: 1
> test-checkpointing.R: 2 3 4
> test-checkpointing.R: 5
> test-checkpointing.R: 6
> test-checkpointing.R: 7
> test-checkpointing.R: 8
> test-checkpointing.R: 9
> test-checkpointing.R: 10 11
> test-checkpointing.R: 12
> test-checkpointing.R: 13
> test-checkpointing.R: 14
> test-bridge-target.stats.R: 5
> test-checkpointing.R: 15 16
> test-checkpointing.R: .
> test-checkpointing.R: Bridging finished.
> test-checkpointing.R:
> test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check
> test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
> test-bridge-target.stats.R: 6
> test-bridge-target.stats.R: 7
> test-bridge-target.stats.R: 8
> test-bridge-target.stats.R: 9
> test-bridge-target.stats.R: 10
> test-bridge-target.stats.R: 11
> test-bridge-target.stats.R: 12
> test-bridge-target.stats.R: 13
> test-bridge-target.stats.R: 14
> test-bridge-target.stats.R: 15
> test-bridge-target.stats.R: 16
> test-bridge-target.stats.R: .
> test-bridge-target.stats.R: Bridging finished.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constrain-degrees-edges.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constrain-degrees-edges.R: Iteration 1 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:5.2e-06
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.05768.
> test-constrain-degrees-edges.R: Iteration 2 of at most 2:
> test-constrain-degrees-edges.R: Convergence test P-value:3.7e-03
> test-constrain-degrees-edges.R: 1
> test-constrain-degrees-edges.R: The log-likelihood improved by 0.09813.
> test-constrain-degrees-edges.R: Finished CD.
> test-constrain-degrees-edges.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-degrees-edges.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondInDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-constrain-blockdiag.R:
> test-constrain-blockdiag.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-constrain-blockdiag.R: * 'news(package="ergm.count")' for changes since last version
> test-constrain-blockdiag.R: * 'citation("ergm.count")' for citation information
> test-constrain-blockdiag.R: * 'https://statnet.org' for help, support, and other information
> test-constrain-blockdiag.R:
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constrain-dind.R: Iteration 1 of at most 60:
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: 1 Optimizing with step length 1.0000.
> test-constrain-dind.R: The log-likelihood improved by 0.0020.
> test-constrain-dind.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constrain-dind.R: Finished MCMLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-constrain-dind.R: Bridging between the dyad-independent submodel and the full model...
> test-constrain-dind.R: Setting up bridge sampling...
> test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Using 16 bridges: 1
> test-constrain-dind.R: 2
> test-constrain-dind.R: 3
> test-constrain-dind.R: 4
> test-constrain-dind.R: 5
> test-constrain-dind.R: 6
> test-constrain-dind.R: 7
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: 8
> test-constrain-dind.R: 9
> test-constrain-dind.R: 10
> test-constrain-dind.R: 11
> test-constrain-dind.R: 12
> test-constrain-dind.R: 13
> test-constrain-dind.R: 14 15 16
> test-constrain-dind.R: .
> test-constrain-dind.R: Bridging finished.
> test-constrain-dind.R:
> test-constrain-dind.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constrain-dind.R: for degeneracy, use the mcmc.diagnostics() function.
> test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R:
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constrain-dind.R: Obtaining the responsible dyads.
> test-constrain-dind.R: Evaluating the predictor and response matrix.
> test-constrain-dind.R: Maximizing the pseudolikelihood.
> test-constrain-dind.R: Finished MPLE.
> test-constrain-dind.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R:
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by 0.0007.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-drop.R: Fitting the dyad-independent submodel...
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Using 16 bridges: 1 2
> test-drop.R: 3
> test-drop.R: 4
> test-drop.R: 5 6
> test-drop.R: 7
> test-drop.R: 8
> test-drop.R: 9 10
> test-drop.R: 11
> test-drop.R: 12
> test-drop.R: 13
> test-drop.R: 14
> test-drop.R: 15
> test-drop.R: 16
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 10:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-constraints.R: Obtaining the responsible dyads.
> test-constraints.R: Evaluating the predictor and response matrix.
> test-constraints.R: Maximizing the pseudolikelihood.
> test-constraints.R: Finished MPLE.
> test-constraints.R: Evaluating log-likelihood at the estimate.
> test-constraints.R:
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constraints.R: All terms are either offsets or extreme values. No optimization is performed.
> test-constraints.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Using 16 bridges: 1
> test-constraints.R: 2
> test-constraints.R: 3
> test-constraints.R: 4
> test-constraints.R: 5
> test-constraints.R: 6
> test-constraints.R: 7
> test-constraints.R: 8
> test-constraints.R: 9
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by < 0.0001.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-constraints.R: 10
> test-drop.R: Fitting the dyad-independent submodel...
> test-constraints.R: 11
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-constraints.R: 12
> test-drop.R: Using 16 bridges: 1
> test-constraints.R: 13
> test-constraints.R: 14
> test-constraints.R: 15
> test-constraints.R: 16
> test-drop.R: 2
> test-constraints.R: .
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-drop.R: 3
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-constraints.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-constraints.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-constraints.R: Iteration 1 of at most 60:
> test-constraints.R: Convergence test P-value:1.1e-05
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.07919.
> test-constraints.R: Iteration 2 of at most 60:
> test-constraints.R: Convergence test P-value:3.7e-02
> test-constraints.R: 1 The log-likelihood improved by 0.01687.
> test-constraints.R: Iteration 3 of at most 60:
> test-constraints.R: Convergence test P-value:7e-01
> test-constraints.R: Convergence detected. Stopping.
> test-constraints.R: 1
> test-constraints.R: The log-likelihood improved by 0.0006029.
> test-constraints.R: Finished CD.
> test-constraints.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-constraints.R: Iteration 1 of at most 60:
> test-drop.R: 4
> test-drop.R: 5
> test-drop.R: 6
> test-constraints.R: 1 Optimizing with step length 1.0000.
> test-constraints.R: The log-likelihood improved by 0.0263.
> test-constraints.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-constraints.R: Finished MCMLE.
> test-constraints.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-drop.R: 7
> test-constraints.R: Using 16 bridges: 1
> test-constraints.R: 2
> test-constraints.R: 3
> test-constraints.R: 4
> test-constraints.R: 5
> test-drop.R: 8
> test-constraints.R: 6 7 8
> test-constraints.R: 9
> test-drop.R: 9
> test-constraints.R: 10
> test-constraints.R: 11
> test-constraints.R: 12
> test-constraints.R: 13
> test-constraints.R: 14
> test-drop.R: 10
> test-constraints.R: 15
> test-constraints.R: 16
> test-constraints.R: .
> test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-constraints.R: model likelihood is only implemented for dyad-independent constraints
> test-constraints.R: at this time. Number of observations is similarly poorly defined. This
> test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-constraints.R: distribution and constraints.
> test-constraints.R:
> test-constraints.R: This model was fit using MCMC. To examine model diagnostics and check
> test-constraints.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: 11
> test-drop.R: 12
> test-drop.R: 13
> test-drop.R: 14
> test-drop.R: 15
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R:
> test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
> test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-proposal-unload.R:
> test-drop.R: 16
> test-drop.R: .
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: Evaluating network in model.
> test-drop.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-drop.R: 'ergm:MH_SPDyad'.
> test-drop.R: Initializing model...
> test-drop.R: Model initialized.
> test-drop.R: Using initial method 'MPLE'.
> test-drop.R: Initial parameters provided by caller: None.
> test-drop.R: number of free parameters: 7
> test-drop.R: number of fixed parameters: 0
> test-drop.R: Observed statistic(s) triangle and kstar5 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Fitting initial model.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Density guard set to 10000 from an initial count of 3 edges.
> test-drop.R:
> test-drop.R: Iteration 1 of at most 3 with parameter:
> test-drop.R: edges triangle degree2 kstar2 kstar5
> test-drop.R: 1.313175 -Inf 6.009173 -4.163221 -Inf
> test-drop.R: gwdegree gwdegree.decay
> test-drop.R: -2.860022 1.666671
> test-drop.R: Starting unconstrained MCMC...
> test-drop.R: Back from unconstrained MCMC.
> test-drop.R: New interval = 512.
> test-drop.R: Average estimating function values:
> test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay
> test-drop.R: -0.29629630 -0.11934156 -0.11934156 -0.57005199 0.06446661
> test-drop.R: Starting MCMLE Optimization...
> test-drop.R: 1 Optimizing with step length 1.0000.
> test-drop.R: Using lognormal metric (see control.ergm function).
> test-drop.R: Optimizing loglikelihood
> test-drop.R: Starting MCMC s.e. computation.
> test-drop.R: The log-likelihood improved by 0.0385.
> test-drop.R: Estimated covariance matrix of the statistics has nullity 3. Effective parameter number adjusted to 2.
> test-drop.R: Test statistic: T^2 = 21.94136, with 2 free parameter(s) and 240.9959 degrees of freedom.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-drop.R: Fitting the dyad-independent submodel...
> test-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-drop.R: Dyad-independent submodel MLE has likelihood -11.02185 at:
> test-drop.R: [1] -2.639057 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
> test-drop.R: [8] 0.000000
> test-drop.R: Bridging between the dyad-independent submodel and the full model...
> test-drop.R: Setting up bridge sampling...
> test-drop.R: Initializing model and proposals...
> test-drop.R: Model and proposals initialized.
> test-drop.R: Using 16 bridges: Running theta=[-1.175967, -Inf, 6.177257,-4.435272, -Inf,-1.386658, 1.950625, 0.000000].
> test-drop.R: Running theta=[-1.270360, -Inf, 5.778724,-4.149126, -Inf,-1.297196, 1.824778, 0.000000].
> test-drop.R: Running theta=[-1.364753, -Inf, 5.380191,-3.862979, -Inf,-1.207735, 1.698931, 0.000000].
> test-drop.R: Running theta=[-1.459146, -Inf, 4.981659,-3.576833, -Inf,-1.118273, 1.573085, 0.000000].
> test-drop.R: Running theta=[-1.553539, -Inf, 4.583126,-3.290686, -Inf,-1.028811, 1.447238, 0.000000].
> test-drop.R: Running theta=[-1.6479316, -Inf, 4.1845933,-3.0045393, -Inf,-0.9393491, 1.3213911, 0.0000000].
> test-drop.R: Running theta=[-1.7423245, -Inf, 3.7860606,-2.7183927, -Inf,-0.8498873, 1.1955444, 0.0000000].
> test-drop.R: Running theta=[-1.8367174, -Inf, 3.3875279,-2.4322461, -Inf,-0.7604255, 1.0696976, 0.0000000].
> test-drop.R: Running theta=[-1.9311104, -Inf, 2.9889952,-2.1460995, -Inf,-0.6709636, 0.9438508, 0.0000000].
> test-drop.R: Running theta=[-2.0255033, -Inf, 2.5904625,-1.8599529, -Inf,-0.5815018, 0.8180040, 0.0000000].
> test-drop.R: Running theta=[-2.1198962, -Inf, 2.1919298,-1.5738063, -Inf,-0.4920400, 0.6921573, 0.0000000].
> test-drop.R: Running theta=[-2.2142891, -Inf, 1.7933971,-1.2876597, -Inf,-0.4025782, 0.5663105, 0.0000000].
> test-drop.R: Running theta=[-2.3086821, -Inf, 1.3948644,-1.0015131, -Inf,-0.3131164, 0.4404637, 0.0000000].
> test-drop.R: Running theta=[-2.4030750, -Inf, 0.9963317,-0.7153665, -Inf,-0.2236545, 0.3146169, 0.0000000].
> test-drop.R: Running theta=[-2.4974679, -Inf, 0.5977990,-0.4292199, -Inf,-0.1341927, 0.1887702, 0.0000000].
> test-drop.R: Running theta=[-2.59186086, -Inf, 0.19926635,-0.14307330, -Inf,-0.04473091, 0.06292339, 0.00000000].
> test-drop.R: .
> test-drop.R: Bridge sampling finished. Collating...
> test-drop.R: Bridging finished.
> test-drop.R:
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (binary, valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ctriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ctriad (binary)
> test-ergm-term-doc.R: Cyclic triples
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: localtriangle(x) (binary)
> test-ergm-term-doc.R: Triangles within neighborhoods
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1) (binary)
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: opentriad (binary)
> test-ergm-term-doc.R: Open triads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: threetrail(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: threepath(keep=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Three-trails
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: triangle(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: triangles(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangles
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: tripercent(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Triangle percentage
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ttriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: ttriad (binary)
> test-ergm-term-doc.R: Transitive triples
> test-ergm-term-doc.R: Found 31 matching ergm terms:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Definitions for term(s) b2factor :
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the
> test-ergm-term-doc.R: unique values of the attr attribute. Each of these statistics
> test-ergm-term-doc.R: gives the number of times a node with that attribute in the second mode of
> test-ergm-term-doc.R: the network appears in an edge. The second mode of a bipartite network
> test-ergm-term-doc.R: object is sometimes known as the "event" mode.
> test-ergm-term-doc.R: Keywords: bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3factor' were found. Try searching with search='b3factor'instead.
> test-ergm-term-doc.R: Found 36 matching ergm terms:
> test-ergm-term-doc.R: Project(formula, mode) (binary)
> test-ergm-term-doc.R: Proj1(formula) (binary)
> test-ergm-term-doc.R: Proj2(formula) (binary)
> test-ergm-term-doc.R: Evaluation on a projection of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr) (binary)
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the first node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
> test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr) (binary)
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovrange(attr) (binary)
> test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
> test-ergm-term-doc.R: Degree range for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
> test-ergm-term-doc.R: Degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2dsp(d) (binary)
> test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
> test-ergm-term-doc.R: Number of distinct neighbor types for the second mode
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2mindegree(d) (binary)
> test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
> test-ergm-term-doc.R: k-stars for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
> test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
> test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
> test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
> test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
> test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: isolatededges (binary)
> test-ergm-term-doc.R: Isolated edges
> test-ergm-term-doc.R: Found 48 matching ergm terms:
> test-ergm-term-doc.R: B(formula, form) (valued)
> test-ergm-term-doc.R: Wrap binary terms for use in valued models
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
> test-ergm-term-doc.R: Impose a curved structure on term parameters
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Exp(formula) (valued)
> test-ergm-term-doc.R: Exponentiate a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: For(...) (valued)
> test-ergm-term-doc.R: A for operator for terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Label(formula, label, pos) (valued)
> test-ergm-term-doc.R: Modify terms' coefficient names
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Log(formula, log0=-1/sqrt(.Machine$double.eps)) (valued)
> test-ergm-term-doc.R: Take a natural logarithm of a network's statistic
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Prod(formulas, label) (valued)
> test-ergm-term-doc.R: A product (or an arbitrary power combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: S(formula, attrs) (valued)
> test-ergm-term-doc.R: Evaluation on an induced subgraph
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Sum(formulas, label) (valued)
> test-ergm-term-doc.R: A sum (or an arbitrary linear combination) of one or more formulas
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (valued)
> test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiff(attr, pow=1, form="sum") (valued)
> test-ergm-term-doc.R: Absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: absdiffcat(attr, base=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Categorical absolute difference in nodal attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atleast(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values greater than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: atmost(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values less than or equal to a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: attrcov(attr, mat, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate by attribute pairing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalties(threshold=0) (valued)
> test-ergm-term-doc.R: Cyclical ties
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: cyclicalweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Cyclical weights
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
> test-ergm-term-doc.R: Difference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edgecov(x, attrname=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Edge covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: edges (valued)
> test-ergm-term-doc.R: nonzero (valued)
> test-ergm-term-doc.R: Number of edges in the network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: equalto(value=0, tolerance=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values equal to a specific value (within tolerance)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: greaterthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly greater than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ininterval(lower=-Inf, upper=+Inf, open=c(TRUE,TRUE)) (valued)
> test-ergm-term-doc.R: Number of dyads whose values are in an interval
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mm(attrs, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Mixing matrix cells and margins
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: mutual(form="min",threshold=0) (valued)
> test-ergm-term-doc.R: Mutuality
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecov(attr, form="sum") (valued)
> test-ergm-term-doc.R: nodemain(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodecovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of undirected dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodefactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeicovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of in-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeifactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for in-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: match(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
> test-ergm-term-doc.R: Uniform homophily and differential homophily
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
> test-ergm-term-doc.R: Nodal attribute mixing
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocov(attr, form="sum") (valued)
> test-ergm-term-doc.R: Main effect of a covariate for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeocovar(center, transform) (valued)
> test-ergm-term-doc.R: Covariance of out-dyad values incident on each actor
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: nodeofactor(attr, base=1, levels=-1, form="sum") (valued)
> test-ergm-term-doc.R: Factor attribute effect for out-edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: receiver(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Receiver effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sender(base=1, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Sender effect
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: smallerthan(threshold=0) (valued)
> test-ergm-term-doc.R: Number of dyads with values strictly smaller than a threshold
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form="sum") (valued)
> test-ergm-term-doc.R: Undirected degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: sum(pow=1) (valued)
> test-ergm-term-doc.R: Sum of dyad values (optionally taken to a power)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: transitiveweights(twopath="min", combine="max", affect="min") (valued)
> test-ergm-term-doc.R: Transitive weights
> test-ergm-term-doc.R: Found 4 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DiscUnif(a,b)
> test-ergm-term-doc.R: Discrete Uniform reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: StdNormal
> test-ergm-term-doc.R: Standard Normal reference
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: Unif(a,b)
> test-ergm-term-doc.R: Continuous Uniform reference
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 1 matching ergm terms:
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference
> test-ergm-term-doc.R: Definitions for term(s) Bernoulli :
> test-ergm-term-doc.R: Bernoulli
> test-ergm-term-doc.R: Bernoulli reference: Specifies each
> test-ergm-term-doc.R: dyad's baseline distribution to be Bernoulli with probability of
> test-ergm-term-doc.R: the tie being 0.5 . This is the only reference measure used
> test-ergm-term-doc.R: in binary mode.
> test-ergm-term-doc.R: Keywords: binary, discrete, finite, nonnegative
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'Cernoulli' were found. Try searching with search='Cernoulli'instead.
> test-ergm-term-doc.R: Found 9 matching ergm terms:
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: b2degrees
> test-ergm-term-doc.R: Preserve the receiver degree for bipartite networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Found 0 matching ergm terms:
> test-ergm-term-doc.R: Found 16 matching ergm terms:
> test-ergm-term-doc.R: Dyads(fix=NULL, vary=NULL)
> test-ergm-term-doc.R: Constrain fixed or varying dyad-independent terms
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
> test-ergm-term-doc.R: Constrain maximum and minimum vertex degree
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blockdiag(attr)
> test-ergm-term-doc.R: Block-diagonal structure constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: blocks(attr=NULL, levels=NULL, levels2=FALSE, b1levels=NULL, b2levels=NULL)
> test-ergm-term-doc.R: Constrain blocks of dyads defined by mixing type on a vertex attribute.
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degreedist
> test-ergm-term-doc.R: Preserve the degree distribution of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: degrees
> test-ergm-term-doc.R: nodedegrees
> test-ergm-term-doc.R: Preserve the degree of each vertex of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise(p01, p10)
> test-ergm-term-doc.R: A soft constraint to adjust the sampled distribution for
> test-ergm-term-doc.R: dyad-level noise with known perturbation probabilities
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: egocentric(attr=NULL, direction="both")
> test-ergm-term-doc.R: Preserve values of dyads incident on vertices with given attribute
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixallbut(free.dyads)
> test-ergm-term-doc.R: Preserve the dyad status in all but the given edges
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: fixedas(fixed.dyads, present, absent)
> test-ergm-term-doc.R: Fix specific dyads
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: hamming
> test-ergm-term-doc.R: Preserve the hamming distance to the given network (BROKEN: Do NOT Use)
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegreedist
> test-ergm-term-doc.R: Preserve the indegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: idegrees
> test-ergm-term-doc.R: Preserve indegree for directed networks
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: observed
> test-ergm-term-doc.R: Preserve the observed dyads of the given network
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegreedist
> test-ergm-term-doc.R: Preserve the outdegree distribution
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: odegrees
> test-ergm-term-doc.R: Preserve outdegree for directed networks
> test-ergm-term-doc.R: Definitions for term(s) b1degrees :
> test-ergm-term-doc.R: b1degrees
> test-ergm-term-doc.R: Preserve the actor degree for bipartite networks: For bipartite networks, preserve the degree for the first mode of each vertex of the given
> test-ergm-term-doc.R: network, while allowing the degree for the second mode to vary.
> test-ergm-term-doc.R: Keywords: bipartite
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No terms named 'b3degrees' were found. Try searching with search='b3degrees'instead.
> test-ergm-term-doc.R: Found 2 matching ergm proposals:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R: Found 5 matching ergm proposals:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: DistRLE
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Found 0 matching ergm proposals:
> test-ergm-term-doc.R: Found 18 matching ergm proposals:
> test-ergm-term-doc.R: BDStratTNT
> test-ergm-term-doc.R: TNT proposal with degree bounds, stratification, and a blocks constraint
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB1Degree
> test-ergm-term-doc.R: MHp for b1degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondB2Degree
> test-ergm-term-doc.R: MHp for b2degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegree
> test-ergm-term-doc.R: MHp for degree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeDist
> test-ergm-term-doc.R: MHp for degreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondDegreeMix
> test-ergm-term-doc.R: MHp for degree mix constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegree
> test-ergm-term-doc.R: MHp for idegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondInDegreeDist
> test-ergm-term-doc.R: MHp for idegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegree
> test-ergm-term-doc.R: MHp for odegree constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: CondOutDegreeDist
> test-ergm-term-doc.R: MHp for odegreedist constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: ConstantEdges
> test-ergm-term-doc.R: MHp for edges constraints
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingConstantEdges
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: HammingTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: SPDyad
> test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused
> test-ergm-term-doc.R: proposal
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: TNT
> test-ergm-term-doc.R: Default MH algorithm
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoise
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: dyadnoiseTNT
> test-ergm-term-doc.R: TODO
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Definitions for proposal(s) randomtoggle :
> test-ergm-term-doc.R: randomtoggle
> test-ergm-term-doc.R: Propose a randomly selected dyad to toggle: Propose a randomly selected dyad to toggle
> test-ergm-term-doc.R: Reference: Bernoulli Class: cross-sectional
> test-ergm-term-doc.R: May Enforce: .dyads bd
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: No proposals named 'mandomtoggle' were found. Try searching with search='mandomtoggle'instead.
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm-term-doc.R:
> test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version
> test-ergm-term-doc.R: * 'citation("ergm.count")' for citation information
> test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information
> test-ergm-term-doc.R:
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergmMPLE.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-ergmMPLE.R: Obtaining the responsible dyads.
> test-ergmMPLE.R: Evaluating the predictor and response matrix.
> test-ergmMPLE.R: Maximizing the pseudolikelihood.
> test-ergmMPLE.R: Finished MPLE.
> test-ergmMPLE.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gflomiss.R: Obtaining the responsible dyads.
> test-gflomiss.R: Evaluating the predictor and response matrix.
> test-gflomiss.R: Maximizing the pseudolikelihood.
> test-gflomiss.R: Finished MPLE.
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-gflomiss.R: 1 Optimizing with step length 1.0000.
> test-gflomiss.R: The log-likelihood improved by 0.0001.
> test-ergm.bridge.llr.R: 7
> test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: 8
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-gflomiss.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 9
> test-gflomiss.R: 2
> test-gflomiss.R: 3
> test-ergm.bridge.llr.R: 10
> test-gflomiss.R: 4
> test-gflomiss.R: 5
> test-gflomiss.R: 6
> test-ergm.bridge.llr.R: 11
> test-gflomiss.R: 7
> test-gflomiss.R: 8
> test-ergm.bridge.llr.R: 12
> test-gflomiss.R: 9
> test-gflomiss.R: 10
> test-ergm.bridge.llr.R: 13
> test-gflomiss.R: 11
> test-gflomiss.R: 12
> test-gflomiss.R: 13
> test-ergm.bridge.llr.R: 14
> test-gflomiss.R: 14
> test-gflomiss.R: 15
> test-gflomiss.R: 16
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 15
> test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-ergm.bridge.llr.R: 16
> test-gflomiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gflomiss.R: 1
> test-gflomiss.R: Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 2
> test-gflomiss.R: The log-likelihood improved by 0.0172.
> test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gflomiss.R: Finished MCMLE.
> test-gflomiss.R: Evaluating log-likelihood at the estimate.
> test-gflomiss.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
> test-gflomiss.R: Setting up bridge sampling...
> test-gflomiss.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 5
> test-gflomiss.R: 2
> test-gflomiss.R: 3
> test-ergm.bridge.llr.R: 6
> test-gflomiss.R: 4 5 6
> test-gflomiss.R: 7
> test-ergm.bridge.llr.R: 7
> test-gflomiss.R: 8 9
> test-gflomiss.R: 10
> test-gflomiss.R: 11
> test-gflomiss.R: 12
> test-ergm.bridge.llr.R: 8
> test-gflomiss.R: 13
> test-gflomiss.R: 14
> test-gflomiss.R: 15
> test-gflomiss.R: 16
> test-ergm.bridge.llr.R: 9
> test-gflomiss.R: .
> test-gflomiss.R: Bridging finished.
> test-gflomiss.R:
> test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-gmonkmiss.R: odegree3 odegree4 odegree5 odegree6
> test-gmonkmiss.R: 1 5 7 5
> test-gmonkmiss.R: idegree2 idegree3 idegree4 idegree5 idegree6 idegree7 idegree8 idegree10
> test-gmonkmiss.R: 3 5 1 3 2 1 1 1
> test-gmonkmiss.R: idegree11
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 12
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-ergm.bridge.llr.R: 13
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-ergm.bridge.llr.R: 14
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.6245.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 5
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-gmonkmiss.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-gmonkmiss.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-gmonkmiss.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-gmonkmiss.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-gmonkmiss.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 6
> test-gmonkmiss.R: Convergence test P-value:3.3e-34
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.4205.
> test-gmonkmiss.R: Iteration 2 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.8e-14
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.1501.
> test-gmonkmiss.R: Iteration 3 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2e-04
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.03536.
> test-gmonkmiss.R: Iteration 4 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:1.6e-01
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.007343.
> test-gmonkmiss.R: Iteration 5 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:2.4e-01
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: The log-likelihood improved by 0.00569.
> test-gmonkmiss.R: Iteration 6 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:9.9e-02
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.00904.
> test-gmonkmiss.R: Iteration 7 of at most 60:
> test-gmonkmiss.R: Convergence test P-value:7.6e-01
> test-gmonkmiss.R: Convergence detected. Stopping.
> test-gmonkmiss.R: 1 The log-likelihood improved by 0.001102.
> test-gmonkmiss.R: Finished CD.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 7
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-gmonkmiss.R: 1
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.4514.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 12
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-ergm.bridge.llr.R: 15
> test-gmonkmiss.R: 1
> test-ergm.bridge.llr.R: 16
> test-gmonkmiss.R: Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.0105.
> test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gmonkmiss.R: Obtaining the responsible dyads.
> test-gmonkmiss.R: Evaluating the predictor and response matrix.
> test-gmonkmiss.R: Maximizing the pseudolikelihood.
> test-gmonkmiss.R: Finished MPLE.
> test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gmonkmiss.R: Iteration 1 of at most 3:
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
> test-gmonkmiss.R: The log-likelihood improved by 0.7035.
> test-gmonkmiss.R: Estimating equations are not within tolerance region.
> test-gmonkmiss.R: Iteration 2 of at most 3:
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 8
> test-gmonkmiss.R: The log-likelihood improved by 0.0078.
> test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-gmonkmiss.R: Finished MCMLE.
> test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 9
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 10
> test-ergm.bridge.llr.R: 11
> test-ergm.bridge.llr.R: 12
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-gof.R:
> test-ergm.bridge.llr.R: 13
> test-ergm.bridge.llr.R: 14
> test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.R: Obtaining the responsible dyads.
> test-gof.R: Evaluating the predictor and response matrix.
> test-gof.R: Maximizing the pseudolikelihood.
> test-gof.R: Finished MPLE.
> test-gof.R: Evaluating log-likelihood at the estimate.
> test-ergm.bridge.llr.R: 15
> test-ergm.bridge.llr.R: 16
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
> test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
> test-ergm.bridge.llr.R: Setting up bridge sampling...
> test-ergm.bridge.llr.R: Using 16 bridges: 1
> test-ergm.bridge.llr.R: 2
> test-ergm.bridge.llr.R: 3
> test-ergm.bridge.llr.R: 4
> test-ergm.bridge.llr.R: 5
> test-ergm.bridge.llr.R: 6
> test-ergm.bridge.llr.R: 7
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: 8
> test-ergm.bridge.llr.R: 9
> test-ergm.bridge.llr.R: 10
> test-metrics.R: 1 Optimizing with step length 0.4613.
> test-metrics.R: The log-likelihood improved by 4.1429.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-ergm.bridge.llr.R: 11
> test-metrics.R: 1 Optimizing with step length 0.8364.
> test-ergm.bridge.llr.R: 12
> test-metrics.R: The log-likelihood improved by 4.7215.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-metrics.R: 1
> test-ergm.bridge.llr.R: 13
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 1.1346.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-ergm.bridge.llr.R: 14
> test-metrics.R: The log-likelihood improved by 0.1129.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-ergm.bridge.llr.R: 15
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0037.
> test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-ergm.bridge.llr.R: 16
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-ergm.bridge.llr.R: .
> test-ergm.bridge.llr.R: Bridging finished.
> test-metrics.R: 1 Optimizing with step length 0.4018.
> test-metrics.R: The log-likelihood improved by 3.2780.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
> test-miss-dep.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
> test-miss-dep.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss-dep.R: Iteration 1 of at most 60:
> test-miss-dep.R: Convergence test P-value:2.2e-47
> test-miss-dep.R: 1 The log-likelihood improved by 1.504.
> test-miss-dep.R: Iteration 2 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.6020.
> test-metrics.R: The log-likelihood improved by 3.4584.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: Convergence test P-value:2.1e-22
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.5752.
> test-miss-dep.R: Iteration 3 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.2132.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: Convergence test P-value:4.7e-04
> test-miss-dep.R: 1 The log-likelihood improved by 0.0516.
> test-miss-dep.R: Iteration 4 of at most 60:
> test-miss-dep.R: Convergence test P-value:2.1e-03
> test-miss-dep.R: 1 The log-likelihood improved by 0.03965.
> test-miss-dep.R: Iteration 5 of at most 60:
> test-miss-dep.R: Convergence test P-value:2.9e-01
> test-miss-dep.R: 1 The log-likelihood improved by 0.004546.
> test-miss-dep.R: Iteration 6 of at most 60:
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0377.
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss-dep.R: Convergence test P-value:7.4e-04
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by 0.0461.
> test-miss-dep.R: Iteration 7 of at most 60:
> test-miss-dep.R: Convergence test P-value:2e-02
> test-miss-dep.R: 1 The log-likelihood improved by 0.02163.
> test-miss-dep.R: Iteration 8 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss-dep.R: Convergence test P-value:1.4e-01
> test-miss-dep.R: 1 The log-likelihood improved by 0.008556.
> test-miss-dep.R: Iteration 9 of at most 60:
> test-miss-dep.R: Convergence test P-value:9.1e-01
> test-miss-dep.R: Convergence detected. Stopping.
> test-miss-dep.R: 1
> test-miss-dep.R: The log-likelihood improved by < 0.0001.
> test-miss-dep.R: Finished CD.
> test-miss-dep.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss-dep.R: Iteration 1 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.4158.
> test-metrics.R: The log-likelihood improved by 1.9115.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-metrics.R: 1 Optimizing with step length 0.4804.
> test-metrics.R: The log-likelihood improved by 2.5519.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss-dep.R: Post-burnin sample is constant; returning.
> test-miss-dep.R: 1 Optimizing with step length 1.0000.
> test-miss-dep.R: The log-likelihood improved by 0.0082.
> test-miss-dep.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss-dep.R: Finished MCMLE.
> test-miss-dep.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 2.9415.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss-dep.R: Using 16 bridges: 1
> test-miss-dep.R: 2
> test-miss-dep.R: 3
> test-miss-dep.R: 4
> test-miss-dep.R: 5
> test-miss-dep.R: 6
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.1226.
> test-miss-dep.R: 7
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss-dep.R: 8 9
> test-miss-dep.R: 10 11
> test-miss-dep.R: 12
> test-miss-dep.R: 13
> test-miss-dep.R: 14 15
> test-miss-dep.R: 16
> test-miss-dep.R: .
> test-miss-dep.R: Note: The constraint on the sample space is not dyad-independent. Null
> test-miss-dep.R: model likelihood is only implemented for dyad-independent constraints
> test-miss-dep.R: at this time. Number of observations is similarly poorly defined. This
> test-miss-dep.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
> test-miss-dep.R: AIC, BIC, etc.) is only valid between models with the same reference
> test-miss-dep.R: distribution and constraints.
> test-miss-dep.R:
> test-miss-dep.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss-dep.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: n=20, density=0.1, missing=0.1
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:3e-13
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.2096.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2 3 4 5 6 7 8
> test-metrics.R: 9
> test-metrics.R: 10
> test-metrics.R: 11 Optimizing with step length 0.3934.
> test-miss.CD.R: Convergence test P-value:1.4e-12
> test-miss.CD.R: 1
> test-metrics.R: The log-likelihood improved by 4.1457.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 0.1974.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:5.8e-11
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.341.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:4e-15
> test-miss.CD.R: 1 The log-likelihood improved by 0.1744.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-16
> test-miss.CD.R: 1 The log-likelihood improved by 0.6862.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.3e-15
> test-miss.CD.R: 1 The log-likelihood improved by 0.3025.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.5e-19
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.1888.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss.CD.R: Convergence test P-value:2.3e-17
> test-miss.CD.R: 1
> test-metrics.R: The log-likelihood improved by 2.8651.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: The log-likelihood improved by 0.2862.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.6e-07
> test-miss.CD.R: 1 The log-likelihood improved by 0.2007.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.1e-02
> test-miss.CD.R: 1 The log-likelihood improved by 0.03854.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-05
> test-miss.CD.R: 1 The log-likelihood improved by 0.1259.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.0004877.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-metrics.R: 1
> test-metrics.R: Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.3360.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.5e-68
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.7099.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.6e-64
> test-miss.CD.R: 1 The log-likelihood improved by 0.5925.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0827.
> test-miss.CD.R: Convergence test P-value:9.6e-53
> test-miss.CD.R: 1 The log-likelihood improved by 0.6327.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.1e-37
> test-miss.CD.R: 1 The log-likelihood improved by 0.8025.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.4e-30
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.583.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-19
> test-miss.CD.R: 1 The log-likelihood improved by 0.6591.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.2e-04
> test-miss.CD.R: 1 The log-likelihood improved by 0.05663.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.4e-01
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.007268.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.1e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-54
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.5854.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.4e-52
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.6164.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.5e-46
> test-miss.CD.R: 1 The log-likelihood improved by 0.6486.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2 3
> test-metrics.R: 4 5
> test-metrics.R: 6
> test-metrics.R: 7 8
> test-miss.CD.R: Convergence test P-value:4.6e-32
> test-miss.CD.R: 1
> test-metrics.R: 9 10
> test-metrics.R: 11 Optimizing with step length 0.3701.
> test-miss.CD.R: The log-likelihood improved by 1.361.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-metrics.R: The log-likelihood improved by 2.3554.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.8e-14
> test-miss.CD.R: 1 The log-likelihood improved by 0.379.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-miss.CD.R: Convergence test P-value:4.6e-03
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.05118.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.9e-02
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.03478.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.5e-01
> test-miss.CD.R: Convergence detected. Stopping.
> test-miss.CD.R: 1
> test-miss.CD.R: The log-likelihood improved by 0.001186.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Network statistics:
> test-miss.CD.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.CD.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.CD.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.CD.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.CD.R: 0 0 0 0 0 0 0
> test-miss.CD.R: Correct estimate = -2.028148
> test-metrics.R: 1 2 3 4
> test-metrics.R: 5 6 7 8 9 10 11 12 Optimizing with step length 0.5218.
> test-metrics.R: The log-likelihood improved by 2.9696.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-miss.CD.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-283
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.712.
> test-miss.CD.R: Iteration 2 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2 3 Optimizing with step length 0.8048.
> test-metrics.R: The log-likelihood improved by 1.8226.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-235
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by 1.493.
> test-miss.CD.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:9.3e-184
> test-miss.CD.R: 1 2
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss.CD.R: The log-likelihood improved by 1.167.
> test-miss.CD.R: Iteration 4 of at most 60:
> test-metrics.R: The log-likelihood improved by 0.2705.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.9e-150
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 5 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 6 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 0.0012.
> test-metrics.R: Convergence test p-value: 0.0099. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.5e-147
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 7 of at most 60:
> test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-metrics.R: Iteration 1 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.2e-155
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 8 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.8e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 9 of at most 60:
> test-miss.CD.R: Convergence test P-value:1e-154
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 10 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.5e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-metrics.R: 1
> test-metrics.R: 2
> test-metrics.R: 3 4
> test-metrics.R: 5 6 7 8 9 10
> test-metrics.R: 11 12
> test-metrics.R: 13
> test-metrics.R: Optimizing with step length 0.4397.
> test-metrics.R: The log-likelihood improved by 3.0119.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 2 of at most 60:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 11 of at most 60:
> test-miss.CD.R: Convergence test P-value:8e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 12 of at most 60:
> test-miss.CD.R: Convergence test P-value:6.7e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 13 of at most 60:
> test-metrics.R: 1
> test-metrics.R: 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.6225.
> test-metrics.R: The log-likelihood improved by 3.6934.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 3 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 14 of at most 60:
> test-miss.CD.R: Convergence test P-value:7.9e-162
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-metrics.R: The log-likelihood improved by 1.0488.
> test-metrics.R: Estimating equations are not within tolerance region.
> test-metrics.R: Iteration 4 of at most 60:
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 15 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.3e-158
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 16 of at most 60:
> test-metrics.R: 1 Optimizing with step length 1.0000.
> test-miss.CD.R: Convergence test P-value:1.1e-159
> test-miss.CD.R: 1
> test-metrics.R: The log-likelihood improved by 0.0821.
> test-miss.CD.R: 2
> test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-metrics.R: Finished MCMLE.
> test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check
> test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 17 of at most 60:
> test-miss.R: n=20, density=0.1, missing=0.05
> test-miss.CD.R: Convergence test P-value:3.3e-159
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 18 of at most 60:
> test-miss.R: Correct estimate = -2.118156 with log-likelihood -120.6883 .
> test-miss.CD.R: Convergence test P-value:6.6e-157
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 19 of at most 60:
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: MPLE estimate = -2.118156 with log-likelihood -120.6883 OK.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.CD.R: Convergence test P-value:2.5e-163
> test-miss.CD.R: 1 2
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 20 of at most 60:
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 26 dyads is required.
> test-miss.R: Imputing 3 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 41 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.118156
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:3.5e-156
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 21 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.3e-153
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 22 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1e-157
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 23 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 49.45267
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5
> test-miss.R: 6 7 8 9 10 11 12
> test-miss.R: Optimizing with step length 0.4099.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.5936.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.374066
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.4e-160
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 24 of at most 60:
> test-miss.CD.R: Convergence test P-value:2.1e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 25 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.CD.R: Convergence test P-value:1.3e-150
> test-miss.CD.R: 1 2
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 26 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.36008
> test-miss.CD.R: Convergence test P-value:4.5e-158
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.4981.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.2702.
> test-miss.R: Distance from origin on tolerance region scale: 192.073 (previously 422.077).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.CD.R: 1 2
> test-miss.R: edges
> test-miss.R: -1.647302
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 27 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.8e-154
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 28 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.6e-160
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 29 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 17.50766
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3
> test-miss.R: 4 5
> test-miss.R: Optimizing with step length 0.7736.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 2.0776.
> test-miss.R: Distance from origin on tolerance region scale: 71.38353 (previously 259.1767).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.95412
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.9e-157
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 30 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 5.621399
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.3258.
> test-miss.R: Distance from origin on tolerance region scale: 6.488425 (previously 62.9371).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 5 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.070021
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 31 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.CD.R: Convergence test P-value:1.9e-155
> test-miss.CD.R: 1 2
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 32 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 32.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 1.853909
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0589.
> test-miss.R: Distance from origin on tolerance region scale: 1.17581 (previously 10.81058).
> test-miss.R: Test statistic: T^2 = 11.54078, with 1 free parameter(s) and 179.1884 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0008. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-miss.CD.R: Convergence test P-value:1.5e-156
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 33 of at most 60:
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.CD.R: Convergence test P-value:1e-153
> test-miss.CD.R: 1 2
> test-miss.R: Dyad-independent submodel MLE has likelihood -120.6883 at:
> test-miss.R: [1] -2.118156 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 34 of at most 60:
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.CD.R: Convergence test P-value:2.5e-165
> test-miss.CD.R: 1 2
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-2.133081, 0.000000].
> test-miss.R: Running theta=[-2.132118, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 35 of at most 60:
> test-miss.R: Running theta=[-2.131155, 0.000000].
> test-miss.R: Running theta=[-2.130192, 0.000000].
> test-miss.R: Running theta=[-2.129229, 0.000000].
> test-miss.R: Running theta=[-2.128267, 0.000000].
> test-miss.R: Running theta=[-2.127304, 0.000000].
> test-miss.R: Running theta=[-2.126341, 0.000000].
> test-miss.R: Running theta=[-2.125378, 0.000000].
> test-miss.CD.R: Convergence test P-value:3.7e-164
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.124415, 0.000000].
> test-miss.R: Running theta=[-2.123452, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 36 of at most 60:
> test-miss.R: Running theta=[-2.122489, 0.000000].
> test-miss.R: Running theta=[-2.121526, 0.000000].
> test-miss.R: Running theta=[-2.120563, 0.000000].
> test-miss.R: Running theta=[-2.1196, 0.0000].
> test-miss.R: Running theta=[-2.118637, 0.000000].
> test-miss.CD.R: Convergence test P-value:3.5e-165
> test-miss.CD.R: 1 2
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.009575496) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.119005, 0.000000].
> test-miss.R: Running theta=[-2.119968, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 37 of at most 60:
> test-miss.R: Running theta=[-2.120931, 0.000000].
> test-miss.R: Running theta=[-2.121894, 0.000000].
> test-miss.R: Running theta=[-2.122857, 0.000000].
> test-miss.R: Running theta=[-2.12382, 0.00000].
> test-miss.CD.R: Convergence test P-value:3.8e-167
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.124783, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 38 of at most 60:
> test-miss.R: Running theta=[-2.125746, 0.000000].
> test-miss.R: Running theta=[-2.126709, 0.000000].
> test-miss.R: Running theta=[-2.127671, 0.000000].
> test-miss.R: Running theta=[-2.128634, 0.000000].
> test-miss.R: Running theta=[-2.129597, 0.000000].
> test-miss.R: Running theta=[-2.13056, 0.00000].
> test-miss.R: Running theta=[-2.131523, 0.000000].
> test-miss.R: Running theta=[-2.132486, 0.000000].
> test-miss.CD.R: Convergence test P-value:4.3e-166
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.133449, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.007542247) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132854, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 39 of at most 60:
> test-miss.R: Running theta=[-2.131891, 0.000000].
> test-miss.R: Running theta=[-2.130928, 0.000000].
> test-miss.R: Running theta=[-2.129965, 0.000000].
> test-miss.R: Running theta=[-2.129002, 0.000000].
> test-miss.R: Running theta=[-2.128039, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.8e-166
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.127076, 0.000000].
> test-miss.R: Running theta=[-2.126113, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 40 of at most 60:
> test-miss.R: Running theta=[-2.125151, 0.000000].
> test-miss.R: Running theta=[-2.124188, 0.000000].
> test-miss.R: Running theta=[-2.123225, 0.000000].
> test-miss.R: Running theta=[-2.122262, 0.000000].
> test-miss.R: Running theta=[-2.121299, 0.000000].
> test-miss.R: Running theta=[-2.120336, 0.000000].
> test-miss.CD.R: Convergence test P-value:1.4e-154
> test-miss.CD.R: 1
> test-miss.R: Running theta=[-2.119373, 0.000000].
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 41 of at most 60:
> test-miss.R: Running theta=[-2.11841, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.006188692) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.118778, 0.000000].
> test-miss.R: Running theta=[-2.119741, 0.000000].
> test-miss.R: Running theta=[-2.120704, 0.000000].
> test-miss.R: Running theta=[-2.121667, 0.000000].
> test-miss.R: Running theta=[-2.12263, 0.00000].
> test-miss.CD.R: Convergence test P-value:1.3e-139
> test-miss.CD.R: 1
> test-miss.R: Running theta=[-2.123593, 0.000000].
> test-miss.CD.R: 2
> test-miss.R: Running theta=[-2.124555, 0.000000].
> test-miss.R: Running theta=[-2.125518, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 42 of at most 60:
> test-miss.R: Running theta=[-2.126481, 0.000000].
> test-miss.R: Running theta=[-2.127444, 0.000000].
> test-miss.R: Running theta=[-2.128407, 0.000000].
> test-miss.R: Running theta=[-2.12937, 0.00000].
> test-miss.CD.R: Convergence test P-value:7.6e-160
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.130333, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 43 of at most 60:
> test-miss.R: Running theta=[-2.131296, 0.000000].
> test-miss.R: Running theta=[-2.132259, 0.000000].
> test-miss.R: Running theta=[-2.133222, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005396227) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-2.132626, 0.000000].
> test-miss.R: Running theta=[-2.131664, 0.000000].
> test-miss.R: Running theta=[-2.130701, 0.000000].
> test-miss.CD.R: Convergence test P-value:3e-152
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.129738, 0.000000].
> test-miss.R: Running theta=[-2.128775, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 44 of at most 60:
> test-miss.R: Running theta=[-2.127812, 0.000000].
> test-miss.R: Running theta=[-2.126849, 0.000000].
> test-miss.R: Running theta=[-2.125886, 0.000000].
> test-miss.R: Running theta=[-2.124923, 0.000000].
> test-miss.R: Running theta=[-2.12396, 0.00000].
> test-miss.CD.R: Convergence test P-value:3.1e-154
> test-miss.CD.R: 1 2
> test-miss.R: Running theta=[-2.122997, 0.000000].
> test-miss.R: Running theta=[-2.122035, 0.000000].
> test-miss.R: Running theta=[-2.121072, 0.000000].
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 45 of at most 60:
> test-miss.R: Running theta=[-2.120109, 0.000000].
> test-miss.R: Running theta=[-2.119146, 0.000000].
> test-miss.R: Running theta=[-2.118183, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.CD.R: Convergence test P-value:1.9e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 46 of at most 60:
> test-miss.CD.R: Convergence test P-value:8.8e-148
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 47 of at most 60:
> test-miss.CD.R: Convergence test P-value:1.8e-153
> test-miss.CD.R: 1
> test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 48 of at most 60:
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1728:32768
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 486
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 1.8539 5.6566 0.2566 0.4463
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -8.881 -1.881 2.119 5.119 14.119
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -2.129e-17 1.663e+00 1.067e-01 1.067e-01
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.8807 -1.3807 0.1193 1.1193 4.0693
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. 1.853909e+00 NA
> test-miss.R: test stat. 4.040502e+00 1.632566e+01
> test-miss.R: P-val. 5.333689e-05 7.904382e-05
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 0.50229634
> test-miss.R: Lag 128 0.27968825
> test-miss.R: Lag 192 0.14989734
> test-miss.R: Lag 256 0.08553620
> test-miss.R: Lag 320 0.01488518
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.04999730
> test-miss.R: Lag 128 0.05488745
> test-miss.R: Lag 192 -0.03080175
> test-miss.R: Lag 256 0.03734623
> test-miss.R: Lag 320 0.01289319
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.4658741
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.6413056
> test-miss.R: Joint P-value (lower = worse): 0.5503774
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.2553269
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.7984706
> test-miss.R: Joint P-value (lower = worse): 0.5503774 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -2.133563 with log-likelihood -120.7039 OK.
> test-miss.CD.R: Convergence test P-value:4.8e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 49 of at most 60:
> test-miss.CD.R: Convergence test P-value:6e-154
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 50 of at most 60:
> test-miss.R: Correct estimate = -1.663142 with log-likelihood -79.82064 .
> test-miss.CD.R: Convergence test P-value:1.4e-158
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 51 of at most 60:
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.CD.R: Convergence test P-value:1.2e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.R: 'ergm:MH_SPDyad'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 8 dyads is required.
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 52 of at most 60:
> test-miss.R: Imputing 1 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 30 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -0.6631421
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.2e-161
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 53 of at most 60:
> test-miss.CD.R: Convergence test P-value:5.8e-145
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 54 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:5.8e-163
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 55 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 33.15638
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 4 5 6 7 8 Optimizing with step length 0.5368.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 4.3000.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.146333
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.3e-156
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 56 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:2e-150
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 57 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 14.85185
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 2 3 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 3.2552.
> test-miss.R: Distance from origin on tolerance region scale: 64.83604 (previously 323.1391).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.584689
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:2.4e-151
> test-miss.CD.R: 1
> test-miss.CD.R: 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 58 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:1.2e-168
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 59 of at most 60:
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.600823
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1297.
> test-miss.R: Distance from origin on tolerance region scale: 2.582218 (previously 84.20395).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 4 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -1.684393
> test-miss.R: Starting unconstrained MCMC...
> test-miss.CD.R: Convergence test P-value:3e-159
> test-miss.CD.R: 1 2
> test-miss.CD.R: The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Iteration 60 of at most 60:
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.CD.R: Convergence test P-value:6.5e-157
> test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
> test-miss.CD.R: Finished CD.
> test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 64.
> test-miss.R: New constrained interval = 32.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.2386831
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0011.
> test-miss.R: Distance from origin on tolerance region scale: 0.02175454 (previously 2.583022).
> test-miss.R: Test statistic: T^2 = 16.95471, with 1 free parameter(s) and 192.1073 degrees of freedom.
> test-miss.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -79.82064 at:
> test-miss.R: [1] -1.663142 0.000000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-1.674863, 0.000000].
> test-miss.R: Running theta=[-1.674107, 0.000000].
> test-miss.R: Running theta=[-1.673351, 0.000000].
> test-miss.R: Running theta=[-1.672594, 0.000000].
> test-miss.R: Running theta=[-1.671838, 0.000000].
> test-miss.R: Running theta=[-1.671082, 0.000000].
> test-miss.R: Running theta=[-1.670326, 0.000000].
> test-miss.R: Running theta=[-1.66957, 0.00000].
> test-miss.R: Running theta=[-1.668813, 0.000000].
> test-miss.R: Running theta=[-1.668057, 0.000000].
> test-miss.R: Running theta=[-1.667301, 0.000000].
> test-miss.R: Running theta=[-1.666545, 0.000000].
> test-miss.R: Running theta=[-1.665789, 0.000000].
> test-miss.R: Running theta=[-1.665033, 0.000000].
> test-miss.R: Running theta=[-1.664276, 0.000000].
> test-miss.R: Running theta=[-1.66352, 0.00000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Estimated standard error (0.005483681) above target (0.005). Drawing additional samples.
> test-miss.R: Running theta=[-1.663809, 0.000000].
> test-miss.R: Running theta=[-1.664565, 0.000000].
> test-miss.R: Running theta=[-1.665321, 0.000000].
> test-miss.R: Running theta=[-1.666078, 0.000000].
> test-miss.R: Running theta=[-1.666834, 0.000000].
> test-miss.R: Running theta=[-1.66759, 0.00000].
> test-miss.R: Running theta=[-1.668346, 0.000000].
> test-miss.R: Running theta=[-1.669102, 0.000000].
> test-miss.R: Running theta=[-1.669858, 0.000000].
> test-miss.R: Running theta=[-1.670615, 0.000000].
> test-miss.R: Running theta=[-1.671371, 0.000000].
> test-miss.R: Running theta=[-1.672127, 0.000000].
> test-miss.R: Running theta=[-1.672883, 0.000000].
> test-miss.R: Running theta=[-1.673639, 0.000000].
> test-miss.R: Running theta=[-1.674396, 0.000000].
> test-miss.R: Running theta=[-1.675152, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.2387 5.2156 0.3346 0.3867
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -9.2346 -4.2346 -0.2346 2.7654 11.7154
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 896:16384
> test-miss.R: Thinning interval = 64
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 4.040e-17 1.007e+00 6.463e-02 6.463e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -1.2346 -0.7346 -0.2346 0.7654 2.7154
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.2386831 NA
> test-miss.R: test stat. -0.6087923 0.3706280
> test-miss.R: P-val. 0.5426621 0.5433813
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.000000000
> test-miss.R: Lag 128 0.141730657
> test-miss.R: Lag 256 -0.008044799
> test-miss.R: Lag 384 0.039503814
> test-miss.R: Lag 512 0.016265240
> test-miss.R: Lag 640 0.025525909
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 64 -0.03837238
> test-miss.R: Lag 128 0.06102163
> test-miss.R: Lag 192 0.01817600
> test-miss.R: Lag 256 -0.07663989
> test-miss.R: Lag 320 -0.02107378
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -1.387683
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.1652336
> test-miss.R: Joint P-value (lower = worse): 0.2706448
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: -0.5702328
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.5685198
> test-miss.R: Joint P-value (lower = worse): 0.2706448 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -1.675241 with log-likelihood -79.81794 OK.
> test-miss.R: Correct estimate = -3.157 with log-likelihood -8.355963 .
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: MPLE estimate = -3.157 with log-likelihood -8.355963 OK.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Evaluating network in model.
> test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing constrained Metropolis-Hastings proposal:
> test-miss.R: 'ergm:MH_TNT'.
> test-miss.R: Initializing model...
> test-miss.R: Model initialized.
> test-miss.R: Using initial method 'MPLE'.
> test-miss.R: Initial parameters provided by caller:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: number of free parameters: 1
> test-miss.R: number of fixed parameters: 0
> test-miss.R: Fitting initial model.
> test-miss.R: Imputing 2 dyads is required.
> test-miss.R: Imputing 0 edges at random.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Density guard set to 10000 from an initial count of 2 edges.
> test-miss.R:
> test-miss.R: Iteration 1 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.157
> test-miss.R: Starting unconstrained MCMC...
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 512.
> test-miss.R: New constrained interval = 256.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 2.942387
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.8416.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 2 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -2.729057
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 256.
> test-miss.R: New constrained interval = 128.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: 0.9012346
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1 Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: The log-likelihood improved by 0.1847.
> test-miss.R: Distance from origin on tolerance region scale: 3.678483 (previously 39.20962).
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R:
> test-miss.R: Iteration 3 of at most 60 with parameter:
> test-miss.R: edges
> test-miss.R: -3.138904
> test-miss.R: Starting unconstrained MCMC...
> test-miss.R: Back from unconstrained MCMC.
> test-miss.R: Starting constrained MCMC...
> test-miss.R: Back from constrained MCMC.
> test-miss.R: New interval = 128.
> test-miss.R: New constrained interval = 64.
> test-miss.R: Average estimating function values:
> test-miss.R: edges
> test-miss.R: -0.05349794
> test-miss.R: Starting MCMLE Optimization...
> test-miss.R: 1
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: Using lognormal metric (see control.ergm function).
> test-miss.R: Using log-normal approx with missing (no optim)
> test-miss.R: Starting MCMC s.e. computation.
> test-miss.R: The log-likelihood improved by 0.0008.
> test-miss.R: Distance from origin on tolerance region scale: 0.01512904 (previously 4.293514).
> test-miss.R: Test statistic: T^2 = 22.84874, with 1 free parameter(s) and 256.603 degrees of freedom.
> test-miss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Dyad-independent submodel MLE has likelihood -8.355963 at:
> test-miss.R: [1] -3.157 0.000
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Initializing model and proposals...
> test-miss.R: Model and proposals initialized.
> test-miss.R: Initializing constrained model and proposals...
> test-miss.R: Constrained model and proposals initialized.
> test-miss.R: Using 16 bridges: Running theta=[-3.11196, 0.00000].
> test-miss.R: Running theta=[-3.114866, 0.000000].
> test-miss.R: Running theta=[-3.117772, 0.000000].
> test-miss.R: Running theta=[-3.120678, 0.000000].
> test-miss.R: Running theta=[-3.123584, 0.000000].
> test-miss.R: Running theta=[-3.126489, 0.000000].
> test-miss.R: Running theta=[-3.129395, 0.000000].
> test-miss.R: Running theta=[-3.132301, 0.000000].
> test-miss.R: Running theta=[-3.135207, 0.000000].
> test-miss.R: Running theta=[-3.138113, 0.000000].
> test-miss.R: Running theta=[-3.141018, 0.000000].
> test-miss.R: Running theta=[-3.143924, 0.000000].
> test-miss.R: Running theta=[-3.14683, 0.00000].
> test-miss.R: Running theta=[-3.149736, 0.000000].
> test-miss.R: Running theta=[-3.152642, 0.000000].
> test-miss.R: Running theta=[-3.155548, 0.000000].
> test-miss.R: .
> test-miss.R: Bridge sampling finished. Collating...
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-miss.R: Sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 3584:65536
> test-miss.R: Thinning interval = 256
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: -0.05350 1.39509 0.08949 0.08949
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -2.05761 -1.05761 -0.05761 0.94239 2.94239
> test-miss.R:
> test-miss.R: Constrained sample statistics summary:
> test-miss.R:
> test-miss.R: Iterations = 1792:32768
> test-miss.R: Thinning interval = 128
> test-miss.R: Number of chains = 1
> test-miss.R: Sample size per chain = 243
> test-miss.R:
> test-miss.R: 1. Empirical mean and standard deviation for each variable,
> test-miss.R: plus standard error of the mean:
> test-miss.R:
> test-miss.R: Mean SD Naive SE Time-series SE
> test-miss.R: 9.838e-19 2.335e-01 1.498e-02 1.763e-02
> test-miss.R:
> test-miss.R: 2. Quantiles for each variable:
> test-miss.R:
> test-miss.R: 2.5% 25% 50% 75% 97.5%
> test-miss.R: -0.05761 -0.05761 -0.05761 -0.05761 0.94239
> test-miss.R:
> test-miss.R:
> test-miss.R: Are unconstrained sample statistics significantly different from constrained?
> test-miss.R: edges (Omni)
> test-miss.R: diff. -0.05349794 NA
> test-miss.R: test stat. -0.58650248 0.3476007
> test-miss.R: P-val. 0.55753790 0.5559932
> test-miss.R:
> test-miss.R: Sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R: Constrained sample statistics cross-correlations:
> test-miss.R: edges
> test-miss.R: edges 1
> test-miss.R:
> test-miss.R: Sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 256 -0.07640761
> test-miss.R: Lag 512 0.04674441
> test-miss.R: Lag 768 -0.08703223
> test-miss.R: Lag 1024 -0.01273911
> test-miss.R: Lag 1280 0.08918131
> test-miss.R: Constrained sample statistics auto-correlation:
> test-miss.R: Chain 1
> test-miss.R: edges
> test-miss.R: Lag 0 1.00000000
> test-miss.R: Lag 128 0.01440843
> test-miss.R: Lag 256 -0.06163854
> test-miss.R: Lag 384 -0.05752332
> test-miss.R: Lag 512 0.09381586
> test-miss.R: Lag 640 0.16935966
> test-miss.R:
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 1.222791
> test-miss.R:
> test-miss.R: Individual P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.2214088
> test-miss.R: Joint P-value (lower = worse): 0.530107
> test-miss.R: Sample statistics burn-in diagnostic (Geweke):
> test-miss.R: Chain 1
> test-miss.R:
> test-miss.R: Fraction in 1st window = 0.1
> test-miss.R: Fraction in 2nd window = 0.5
> test-miss.R:
> test-miss.R: edges
> test-miss.R: 0.899493
> test-miss.R:
> test-miss.R: P-values (lower = worse):
> test-miss.R: edges
> test-miss.R: 0.3683901
> test-miss.R: Joint P-value (lower = worse): 0.530107 .
> test-miss.R:
> test-miss.R: Note: MCMC diagnostics shown here are from the last round of
> test-miss.R: simulation, prior to computation of final parameter estimates.
> test-miss.R: Because the final estimates are refinements of those used for this
> test-miss.R: simulation run, these diagnostics may understate model performance.
> test-miss.R: To directly assess the performance of the final model on in-model
> test-miss.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-miss.R: GOF=~model).
> test-miss.R:
> test-miss.R: MCMCMLE estimate = -3.110507 with log-likelihood -8.357166 OK.
> test-miss.R: Network statistics:
> test-miss.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10
> test-miss.R: 50 24 3 0 0 0 0 0 0 0 0
> test-miss.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21
> test-miss.R: 0 0 0 0 0 0 0 0 0 0 0
> test-miss.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28
> test-miss.R: 0 0 0 0 0 0 0
> test-miss.R: Correct estimate = -2.028148
> test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-miss.R: Obtaining the responsible dyads.
> test-miss.R: Evaluating the predictor and response matrix.
> test-miss.R: Maximizing the pseudolikelihood.
> test-miss.R: Finished MPLE.
> test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-miss.R: Iteration 1 of at most 5:
> test-miss.R: 1 2
> test-miss.R: 3 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 2 of at most 5:
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6 7 8 9 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 3 of at most 5:
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-miss.R: 1 2 3
> test-miss.R: 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 4 of at most 5:
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6 7 8 Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Iteration 5 of at most 5:
> test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
> test-miss.R: 1
> test-miss.R: 2 3 4 5 6 7
> test-miss.R: 8
> test-miss.R: 9
> test-miss.R: Optimizing with step length 1.0000.
> test-miss.R: The log-likelihood improved by < 0.0001.
> test-miss.R: Estimating equations are not within tolerance region.
> test-miss.R: Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
> test-miss.R: MCMLE estimation did not converge after 5 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-miss.R: Finished MCMLE.
> test-miss.R: Evaluating log-likelihood at the estimate.
> test-miss.R: Fitting the dyad-independent submodel...
> test-miss.R: Bridging between the dyad-independent submodel and the full model...
> test-miss.R: Setting up bridge sampling...
> test-miss.R: Using 16 bridges: 1
> test-miss.R: 2
> test-miss.R: 3 4
> test-miss.R: 5
> test-miss.R: 6 7 8 9
> test-miss.R: 10
> test-miss.R: 11 12
> test-miss.R: 13 14
> test-miss.R: 15
> test-miss.R: 16
> test-miss.R: .
> test-miss.R: Bridging finished.
> test-miss.R:
> test-miss.R: This model was fit using MCMC. To examine model diagnostics and check
> test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-largenetwork.R:
> test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-largenetwork.R: Obtaining the responsible dyads.
> test-mple-largenetwork.R: Evaluating the predictor and response matrix.
> test-mple-largenetwork.R: Maximizing the pseudolikelihood.
> test-mple-largenetwork.R: Finished MPLE.
> test-mple-largenetwork.R: Evaluating log-likelihood at the estimate.
> test-mple-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-offset.R: Obtaining the responsible dyads.
> test-mple-offset.R: Evaluating the predictor and response matrix.
> test-mple-offset.R: Maximizing the pseudolikelihood.
> test-mple-offset.R: Finished MPLE.
> test-mple-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-offset.R: Iteration 1 of at most 60:
> test-mple-offset.R: 1
> test-mple-offset.R: Optimizing with step length 1.0000.
> test-mple-offset.R: The log-likelihood improved by 0.0040.
> test-mple-offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-mple-offset.R: Finished MCMLE.
> test-mple-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-mple-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-mple-target.R: [1] 350 50 250
> test-mple-target.R: Structural check:
> test-mple-target.R: Mean degree: 1.4 .
> test-mple-target.R: Average degree among nodes with degree 2 or higher: 2.25 .
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-target.R: Obtaining the responsible dyads.
> test-mple-target.R: Evaluating the predictor and response matrix.
> test-mple-target.R: Maximizing the pseudolikelihood.
> test-mple-target.R: Finished MPLE.
> test-mple-target.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-mple-target.R: Iteration 1 of at most 60:
> test-networkLite.R: Loading required package: networkLite
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Estimating Bootstrap Standard Errors using 500 simulated networks.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-mple-cov.R:
> test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-mple-cov.R: Obtaining the responsible dyads.
> test-mple-cov.R: Evaluating the predictor and response matrix.
> test-mple-cov.R: Maximizing the pseudolikelihood.
> test-mple-cov.R: Finished MPLE.
> test-mple-cov.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3 4 5 6 7 8 9 10 11
> test-networkLite.R: 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6
> test-networkLite.R: 7 8
> test-networkLite.R: 9
> test-networkLite.R: 10 11 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0297.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3 4 5 6 7 8 9 10 11
> test-networkLite.R: 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1793.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0302.
> test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4 5 6 7 8
> test-networkLite.R: 9 10
> test-networkLite.R: 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1592.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0101.
> test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4 5 6 7 8
> test-networkLite.R: 9
> test-networkLite.R: 10 11
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0392.
> test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-nodrop.R: 1 Optimizing with step length 1.0000.
> test-nodrop.R: The log-likelihood improved by 0.0005.
> test-nodrop.R: Convergence test p-value: < 0.0001.
> test-nodrop.R: Converged with 99% confidence.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-nodrop.R: 1
> test-nodrop.R: Optimizing with step length 0.2247.
> test-nodrop.R: The log-likelihood improved by 2.2822.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-nodrop.R: 1 Optimizing with step length 0.2817.
> test-nodrop.R: The log-likelihood improved by 2.4734.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nodrop.R: Obtaining the responsible dyads.
> test-nodrop.R: Evaluating the predictor and response matrix.
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-nodrop.R: Maximizing the pseudolikelihood.
> test-nodrop.R: Finished MPLE.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nodrop.R: Iteration 1 of at most 2:
> test-nodrop.R: 1 Optimizing with step length 0.2675.
> test-nodrop.R: The log-likelihood improved by 3.3752.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: Iteration 2 of at most 2:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-nodrop.R: 1 Optimizing with step length 0.3098.
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-nodrop.R: The log-likelihood improved by 2.4783.
> test-nodrop.R: Estimating equations are not within tolerance region.
> test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nodrop.R: Finished MCMLE.
> test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-networkLite.R: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Optimizing with step length 1.0000.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 2 3
> test-networkLite.R: 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonident-test.R: 1
> test-nonident-test.R: Optimizing with step length 1.0000.
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1 Optimizing with step length 0.9410.
> test-networkLite.R: The log-likelihood improved by 5.9387.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonident-test.R: Iteration 1 of at most 1:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.8905.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1368.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 4 of at most 60:
> test-nonident-test.R: 1
> test-nonident-test.R: Optimizing with step length 0.8147.
> test-nonident-test.R: The log-likelihood improved by < 0.0001.
> test-nonident-test.R: Estimating equations are not within tolerance region.
> test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonident-test.R: Finished MCMLE.
> test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: The log-likelihood improved by 0.0176.
> test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 5 of at most 60:
> test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonident-test.R: Obtaining the responsible dyads.
> test-nonident-test.R: Evaluating the predictor and response matrix.
> test-nonident-test.R: Maximizing the pseudolikelihood.
> test-nonident-test.R: Finished MPLE.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-nonunique-names.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-nonunique-names.R: Obtaining the responsible dyads.
> test-nonunique-names.R: Evaluating the predictor and response matrix.
> test-nonunique-names.R: Maximizing the pseudolikelihood.
> test-nonunique-names.R: Finished MPLE.
> test-nonunique-names.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-nonunique-names.R: Iteration 1 of at most 1:
> test-networkLite.R: The log-likelihood improved by 0.0103.
> test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 6 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0419.
> test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-networkLite.R: Obtaining the responsible dyads.
> test-networkLite.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Maximizing the pseudolikelihood.
> test-networkLite.R: Finished MPLE.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 2 3 4 5 6 7 8 9
> test-networkLite.R: 10
> test-networkLite.R: 11
> test-networkLite.R: 12 13 14 15 16
> test-networkLite.R: 17
> test-networkLite.R: 18
> test-networkLite.R: 19
> test-networkLite.R: 20 21 22 23
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1680.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0094.
> test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-nonunique-names.R: 1
> test-nonunique-names.R: Optimizing with step length 1.0000.
> test-nonunique-names.R: The log-likelihood improved by 0.0084.
> test-nonunique-names.R: Convergence test p-value: 0.0480. Not converged with 99% confidence; increasing sample size.
> test-nonunique-names.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-nonunique-names.R: Finished MCMLE.
> test-nonunique-names.R: This model was fit using MCMC. To examine model diagnostics and check
> test-nonunique-names.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-nonunique-names.R: Sample statistics summary:
> test-nonunique-names.R:
> test-nonunique-names.R: Iterations = 2304:44032
> test-nonunique-names.R: Thinning interval = 128
> test-nonunique-names.R: Number of chains = 1
> test-nonunique-names.R: Sample size per chain = 327
> test-nonunique-names.R:
> test-nonunique-names.R: 1. Empirical mean and standard deviation for each variable,
> test-nonunique-names.R: plus standard error of the mean:
> test-nonunique-names.R:
> test-nonunique-names.R: Mean SD Naive SE Time-series SE
> test-nonunique-names.R: edgecov.a -0.2171 3.380 0.1869 0.3114
> test-nonunique-names.R: edgecov.a 0.1346 3.565 0.1971 0.4311
> test-nonunique-names.R:
> test-nonunique-names.R: 2. Quantiles for each variable:
> test-nonunique-names.R:
> test-nonunique-names.R: 2.5% 25% 50% 75% 97.5%
> test-nonunique-names.R: edgecov.a -7 -2 0 2 6.85
> test-nonunique-names.R: edgecov.a -7 -2 0 3 7.00
> test-nonunique-names.R:
> test-nonunique-names.R:
> test-nonunique-names.R: Are sample statistics significantly different from observed?
> test-nonunique-names.R: edgecov.a edgecov.a (Omni)
> test-nonunique-names.R: diff. -0.2171254 0.1345566 NA
> test-nonunique-names.R: test stat. -0.6971750 0.3120903 1.2954084
> test-nonunique-names.R: P-val. 0.4856933 0.7549719 0.5307488
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics cross-correlations:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: edgecov.a 1.0000000 0.6853513
> test-nonunique-names.R: edgecov.a 0.6853513 1.0000000
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics auto-correlation:
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: Lag 0 1.000000000 1.00000000
> test-nonunique-names.R: Lag 128 0.592467538 0.65334235
> test-nonunique-names.R: Lag 256 0.298337375 0.41906307
> test-nonunique-names.R: Lag 384 0.082532656 0.22830518
> test-nonunique-names.R: Lag 512 -0.003477022 0.08811653
> test-nonunique-names.R: Lag 640 -0.090771181 0.03701357
> test-nonunique-names.R:
> test-nonunique-names.R: Sample statistics burn-in diagnostic (Geweke):
> test-nonunique-names.R: Chain 1
> test-nonunique-names.R:
> test-nonunique-names.R: Fraction in 1st window = 0.1
> test-nonunique-names.R: Fraction in 2nd window = 0.5
> test-nonunique-names.R:
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: -0.41748721 0.04619135
> test-nonunique-names.R:
> test-nonunique-names.R: Individual P-values (lower = worse):
> test-nonunique-names.R: edgecov.a edgecov.a
> test-nonunique-names.R: 0.6763221 0.9631577
> test-nonunique-names.R: Joint P-value (lower = worse): 0.8447246
> test-nonunique-names.R:
> test-nonunique-names.R: Note: MCMC diagnostics shown here are from the last round of
> test-nonunique-names.R: simulation, prior to computation of final parameter estimates.
> test-nonunique-names.R: Because the final estimates are refinements of those used for this
> test-nonunique-names.R: simulation run, these diagnostics may understate model performance.
> test-nonunique-names.R: To directly assess the performance of the final model on in-model
> test-nonunique-names.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-nonunique-names.R: GOF=~model).
> test-nonunique-names.R:
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:1e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.01081.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:8.1e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.000224.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 1.0636.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0078.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R:
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:9.2e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by < 0.0001.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.6004.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.1926.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0160.
> test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.0061.
> test-offsets.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-offsets.R: Using 16 bridges:
> test-offsets.R: 1 2
> test-offsets.R: 3 4
> test-offsets.R: 5
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: 6 7
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-offsets.R: 8
> test-offsets.R: 9
> test-offsets.R: 10
> test-offsets.R: 11
> test-offsets.R: 12
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: 13
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-offsets.R: 14 15
> test-offsets.R: 16
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-offsets.R: Obtaining the responsible dyads.
> test-offsets.R: Evaluating the predictor and response matrix.
> test-offsets.R: Maximizing the pseudolikelihood.
> test-offsets.R: Finished MPLE.
> test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-offsets.R: Iteration 1 of at most 2:
> test-networkLite.R: 1
> test-networkLite.R: Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: Convergence test P-value:3e-01
> test-networkLite.R: 1 The log-likelihood improved by 0.004165.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: Convergence test P-value:1.2e-01
> test-networkLite.R: 1
> test-networkLite.R: The log-likelihood improved by 0.009777.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: Convergence test P-value:6.5e-01
> test-networkLite.R: Convergence detected. Stopping.
> test-networkLite.R: 1 The log-likelihood improved by 0.0008154.
> test-networkLite.R: Finished CD.
> test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-networkLite.R: Iteration 1 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.7841.
> test-networkLite.R: Estimating equations are not within tolerance region.
> test-networkLite.R: Iteration 2 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0006.
> test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
> test-networkLite.R: Iteration 3 of at most 60:
> test-networkLite.R: 1 Optimizing with step length 1.0000.
> test-networkLite.R: The log-likelihood improved by 0.0803.
> test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
> test-networkLite.R: Finished MCMLE.
> test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check
> test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
> test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-operators.R: Obtaining the responsible dyads.
> test-operators.R: Evaluating the predictor and response matrix.
> test-operators.R: Maximizing the pseudolikelihood.
> test-operators.R: Finished MPLE.
> test-parallel.R:
> test-parallel.R: Attaching package: 'statnet.common'
> test-parallel.R:
> test-parallel.R: The following objects are masked from 'package:base':
> test-parallel.R:
> test-parallel.R: attr, order, replace
> test-parallel.R:
> test-parallel.R: parallel test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run.
> test-parallel.R: Skipping OpenMP test. This package installation was built without OpenMP support.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-offsets.R: 1 Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.7959.
> test-offsets.R: Estimating equations are not within tolerance region.
> test-offsets.R: Iteration 2 of at most 2:
> test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.R: Obtaining the responsible dyads.
> test-predict.ergm.R: Evaluating the predictor and response matrix.
> test-predict.ergm.R: Maximizing the pseudolikelihood.
> test-predict.ergm.R: Finished MPLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 1
> test-offsets.R: Optimizing with step length 1.0000.
> test-offsets.R: The log-likelihood improved by 0.0207.
> test-offsets.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
> test-offsets.R: Finished MCMLE.
> test-offsets.R: Evaluating log-likelihood at the estimate.
> test-offsets.R: Fitting the dyad-independent submodel...
> test-offsets.R: Bridging between the dyad-independent submodel and the full model...
> test-offsets.R: Setting up bridge sampling...
> test-offsets.R: Using 16 bridges: 1
> test-offsets.R: 2
> test-offsets.R: 3
> test-offsets.R: 4
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 5
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 6
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 7
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 8
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 9
> test-offsets.R: 10
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 11
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 12
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-offsets.R: 13
> test-offsets.R: 14
> test-offsets.R: 15
> test-offsets.R: 16
> test-offsets.R: .
> test-offsets.R: Bridging finished.
> test-offsets.R:
> test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check
> test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
> test-runtime-diags.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-runtime-diags.R: Obtaining the responsible dyads.
> test-runtime-diags.R: Evaluating the predictor and response matrix.
> test-runtime-diags.R: Maximizing the pseudolikelihood.
> test-runtime-diags.R: Finished MPLE.
> test-runtime-diags.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-runtime-diags.R: Iteration 1 of at most 60:
> test-runtime-diags.R: 1 Optimizing with step length 1.0000.
> test-runtime-diags.R: The log-likelihood improved by 0.0414.
> test-runtime-diags.R: Convergence test p-value: 0.0016. Converged with 99% confidence.
> test-runtime-diags.R: Finished MCMLE.
> test-runtime-diags.R: This model was fit using MCMC. To examine model diagnostics and check
> test-runtime-diags.R: for degeneracy, use the mcmc.diagnostics() function.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: 1 Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges: 1 2 3
> test-scoping.R: 4 5 6
> test-scoping.R: 7
> test-scoping.R: 8
> test-scoping.R: 9 10
> test-scoping.R: 11
> test-scoping.R: 12
> test-scoping.R: 13 14
> test-scoping.R: 15 16
> test-scoping.R: .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-scoping.R: Obtaining the responsible dyads.
> test-scoping.R: Evaluating the predictor and response matrix.
> test-scoping.R: Maximizing the pseudolikelihood.
> test-scoping.R: Finished MPLE.
> test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-scoping.R: Iteration 1 of at most 1:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-scoping.R: 1 Optimizing with step length 1.0000.
> test-scoping.R: The log-likelihood improved by 0.2011.
> test-scoping.R: Estimating equations are not within tolerance region.
> test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-scoping.R: Finished MCMLE.
> test-scoping.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-scoping.R: Bridging between the dyad-independent submodel and the full model...
> test-scoping.R: Setting up bridge sampling...
> test-scoping.R: Using 16 bridges: 1
> test-scoping.R: 2
> test-scoping.R: 3 4
> test-scoping.R: 5
> test-scoping.R: 6
> test-scoping.R: 7
> test-scoping.R: 8 9 10
> test-scoping.R: 11
> test-scoping.R: 12 13 14 15 16 .
> test-scoping.R: Bridging finished.
> test-scoping.R:
> test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check
> test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-shrink-into-CH.R: 1 2 3 4 5
> test-shrink-into-CH.R: 6 7 8 9
> test-shrink-into-CH.R: 10
> test-shrink-into-CH.R: 11 12 13 14 15
> test-shrink-into-CH.R: 16 17 18 19 20
> test-shrink-into-CH.R: 1 2 3
> test-shrink-into-CH.R: 4
> test-shrink-into-CH.R: 5
> test-shrink-into-CH.R: 6 7 8 9 10 11 12 13 14 15
> test-shrink-into-CH.R: 16 17 18 19 20
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R:
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R:
> test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-skip.R: Obtaining the responsible dyads.
> test-skip.R: Evaluating the predictor and response matrix.
> test-skip.R: Maximizing the pseudolikelihood.
> test-skip.R: Finished MPLE.
> test-skip.R: Evaluating log-likelihood at the estimate.
> test-skip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-skip.R: Iteration 1 of at most 60:
> test-skip.R: 1 Optimizing with step length 1.0000.
> test-skip.R: The log-likelihood improved by 0.0360.
> test-skip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-skip.R: Finished MCMLE.
> test-skip.R: This model was fit using MCMC. To examine model diagnostics and check
> test-skip.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-snctrl.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-snctrl.R: Obtaining the responsible dyads.
> test-snctrl.R: Evaluating the predictor and response matrix.
> test-snctrl.R: Maximizing the pseudolikelihood.
> test-snctrl.R: Finished MPLE.
> test-snctrl.R: Evaluating log-likelihood at the estimate.
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.7009355 0.2208488
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges triangle
> test-stocapprox.R: -1.6617183 0.1405334
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1 Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0034.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.5333754 -0.1317716 0.6729982
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: edges gwdegree gwdegree.decay
> test-stocapprox.R: -1.57231795 -0.05712682 0.44962020
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-stocapprox.R: Obtaining the responsible dyads.
> test-stocapprox.R: Evaluating the predictor and response matrix.
> test-stocapprox.R: Maximizing the pseudolikelihood.
> test-stocapprox.R: Finished MPLE.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1
> test-stocapprox.R: Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0007.
> test-stocapprox.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.1e-111
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 1.945.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-44
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.5962.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:4e-07
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.06364.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.6e-05
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.04097.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.3e-03
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.01842.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:5.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.002083.
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.743217 0.112619
> test-stocapprox.R: Starting burnin of 16384 steps
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
> test-stocapprox.R: Stochastic Approximation estimate:
> test-stocapprox.R: nonzero transitiveweights.min.max.min
> test-stocapprox.R: -1.7631980 0.1383531
> test-stocapprox.R: Phase 3: 1000 iterations (interval=1024)
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
> test-stocapprox.R: Iteration 1 of at most 60:
> test-stocapprox.R: Convergence test P-value:1.4e-98
> test-stocapprox.R: 1 The log-likelihood improved by 1.862.
> test-stocapprox.R: Iteration 2 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.5e-30
> test-stocapprox.R: 1 The log-likelihood improved by 0.3427.
> test-stocapprox.R: Iteration 3 of at most 60:
> test-stocapprox.R: Convergence test P-value:3e-09
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.08204.
> test-stocapprox.R: Iteration 4 of at most 60:
> test-stocapprox.R: Convergence test P-value:3.9e-02
> test-stocapprox.R: 1
> test-stocapprox.R: The log-likelihood improved by 0.01313.
> test-stocapprox.R: Iteration 5 of at most 60:
> test-stocapprox.R: Convergence test P-value:9.2e-02
> test-stocapprox.R: 1 The log-likelihood improved by 0.009411.
> test-stocapprox.R: Iteration 6 of at most 60:
> test-stocapprox.R: Convergence test P-value:2.6e-01
> test-stocapprox.R: 1
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: The log-likelihood improved by 0.005336.
> test-stocapprox.R: Iteration 7 of at most 60:
> test-stocapprox.R: Convergence test P-value:7.9e-01
> test-stocapprox.R: Convergence detected. Stopping.
> test-stocapprox.R: 1 The log-likelihood improved by 0.0009177.
> test-stocapprox.R: Finished CD.
> test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-stocapprox.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-stocapprox.R: 1 Optimizing with step length 1.0000.
> test-stocapprox.R: The log-likelihood improved by 0.0022.
> test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-stocapprox.R: Finished MCMLE.
> test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check
> test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
> test-stocapprox.R:
> test-stocapprox.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-stocapprox.R: * 'news(package="ergm.count")' for changes since last version
> test-stocapprox.R: * 'citation("ergm.count")' for citation information
> test-stocapprox.R: * 'https://statnet.org' for help, support, and other information
> test-stocapprox.R:
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 60:
> test-target-offset.R: 1 Optimizing with step length 1.0000.
> test-target-offset.R: The log-likelihood improved by 0.0210.
> test-target-offset.R: Convergence test p-value: 0.0002. Converged with 99% confidence.
> test-target-offset.R: Finished MCMLE.
> test-target-offset.R: Evaluating log-likelihood at the estimate.
> test-target-offset.R: Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-target-offset.R: Using 16 bridges: 1
> test-target-offset.R: 2
> test-target-offset.R: 3 4
> test-target-offset.R: 5
> test-target-offset.R: 6
> test-target-offset.R: 7
> test-target-offset.R: 8
> test-target-offset.R: 9
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 10
> test-target-offset.R: 11
> test-target-offset.R: 12
> test-target-offset.R: 13
> test-target-offset.R: 14
> test-target-offset.R: 15
> test-target-offset.R: 16
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 14336:262144
> test-target-offset.R: Thinning interval = 1024
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 0.55556 4.490 0.2880 0.2880
> test-target-offset.R: degree1 0.04527 2.005 0.1286 0.1286
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges -7 -2.5 0 3 9.00
> test-target-offset.R: degree1 -3 -1.0 0 1 4.95
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Are sample statistics significantly different from observed?
> test-target-offset.R: edges degree1 (Omni)
> test-target-offset.R: diff. 0.55555556 0.04526749 NA
> test-target-offset.R: test stat. 1.92893422 0.35200754 9.455405747
> test-target-offset.R: P-val. 0.05373903 0.72483261 0.009922641
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges degree1
> test-target-offset.R: edges 1.0000000 -0.7250142
> test-target-offset.R: degree1 -0.7250142 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges degree1
> test-target-offset.R: Lag 0 1.000000000 1.000000000
> test-target-offset.R: Lag 1024 -0.061427219 0.030194492
> test-target-offset.R: Lag 2048 0.007868535 0.092075103
> test-target-offset.R: Lag 3072 0.018624816 0.047810603
> test-target-offset.R: Lag 4096 -0.047471894 0.007659206
> test-target-offset.R: Lag 5120 -0.073593205 -0.009870131
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-target-offset.R: Chain 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.4498910 -0.1601093
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges degree1
> test-target-offset.R: 0.652789 0.872795
> test-target-offset.R: Joint P-value (lower = worse): 0.8380775
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
> test-target-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-target-offset.R: Obtaining the responsible dyads.
> test-target-offset.R: Evaluating the predictor and response matrix.
> test-target-offset.R: Maximizing the pseudolikelihood.
> test-target-offset.R: Finished MPLE.
> test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-target-offset.R: Iteration 1 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.7297.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 2 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1
> test-target-offset.R: Optimizing with step length 0.6389.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: Iteration 3 of at most 3:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: 1 Optimizing with step length 0.8386.
> test-target-offset.R: The log-likelihood improved by < 0.0001.
> test-target-offset.R: MCMLE estimation did not converge after 3 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-target-offset.R: Finished MCMLE.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
> test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
> test-target-offset.R: Setting up bridge sampling...
> test-target-offset.R: Using 16 bridges:
> test-target-offset.R: 1 2
> test-target-offset.R: 3
> test-target-offset.R: 4
> test-target-offset.R: 5 6
> test-target-offset.R: 7 8 9
> test-target-offset.R: 10
> test-target-offset.R: 11 12
> test-target-offset.R: 13
> test-target-offset.R: 14
> test-target-offset.R: 15
> test-target-offset.R: 16 .
> test-target-offset.R: Bridging finished.
> test-target-offset.R:
> test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-target-offset.R: Sample statistics summary:
> test-target-offset.R:
> test-target-offset.R: Iterations = 3584:65536
> test-target-offset.R: Thinning interval = 256
> test-target-offset.R: Number of chains = 1
> test-target-offset.R: Sample size per chain = 243
> test-target-offset.R:
> test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
> test-target-offset.R: plus standard error of the mean:
> test-target-offset.R:
> test-target-offset.R: Mean SD Naive SE Time-series SE
> test-target-offset.R: edges 12.77778 5.118432 0.32835 0.3283476
> test-target-offset.R: gwdegree 0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R: gwdegree.decay 0.01236 0.002961 0.00019 0.0001293
> test-target-offset.R: degree0 -0.84362 0.386003 0.02476 0.0247621
> test-target-offset.R:
> test-target-offset.R: 2. Quantiles for each variable:
> test-target-offset.R:
> test-target-offset.R: 2.5% 25% 50% 75% 97.5%
> test-target-offset.R: edges 3.000e+00 9.00000 13.00000 16.00000 23.00000
> test-target-offset.R: gwdegree 1.063e-12 1.00000 1.00000 1.00000 1.00000
> test-target-offset.R: gwdegree.decay 5.955e-03 0.01191 0.01191 0.01489 0.01489
> test-target-offset.R: degree0 -1.000e+00 -1.00000 -1.00000 -1.00000 0.00000
> test-target-offset.R:
> test-target-offset.R:
> test-target-offset.R: Sample statistics cross-correlations:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: edges 1.0000000 0.2709648 0.6049364 -0.2709648
> test-target-offset.R: gwdegree 0.2709648 1.0000000 0.5143643 -1.0000000
> test-target-offset.R: gwdegree.decay 0.6049364 0.5143643 1.0000000 -0.5143643
> test-target-offset.R: degree0 -0.2709648 -1.0000000 -0.5143643 1.0000000
> test-target-offset.R:
> test-target-offset.R: Sample statistics auto-correlation:
> test-target-offset.R: Chain 1
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: Lag 0 1.000000000 1.00000000 1.000000000 1.00000000
> test-target-offset.R: Lag 256 0.006056003 0.02865300 -0.034893817 0.02865300
> test-target-offset.R: Lag 512 0.062813023 -0.07862186 0.002608612 -0.07862186
> test-target-offset.R: Lag 768 -0.089332866 -0.07930006 -0.150790861 -0.07930006
> test-target-offset.R: Lag 1024 -0.091496281 -0.07564135 -0.189123113 -0.07564135
> test-target-offset.R: Lag 1280 0.030192390 0.03461402 0.073975025 0.03461402
> test-target-offset.R:
> test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
> test-target-offset.R: Chain 1
> test-target-offset.R:
> test-target-offset.R: Fraction in 1st window = 0.1
> test-target-offset.R: Fraction in 2nd window = 0.5
> test-target-offset.R:
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.05663036 -1.34419649 0.38772965 1.34419649
> test-target-offset.R:
> test-target-offset.R: Individual P-values (lower = worse):
> test-target-offset.R: edges gwdegree gwdegree.decay degree0
> test-target-offset.R: 0.9548397 0.1788849 0.6982161 0.1788849
> test-target-offset.R: Joint P-value (lower = worse): 0.3850888
> test-target-offset.R:
> test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
> test-target-offset.R: simulation, prior to computation of final parameter estimates.
> test-target-offset.R: Because the final estimates are refinements of those used for this
> test-target-offset.R: simulation run, these diagnostics may understate model performance.
> test-target-offset.R: To directly assess the performance of the final model on in-model
> test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject,
> test-target-offset.R: GOF=~model).
> test-target-offset.R:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: 1 Optimizing with step length 1.0000.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-Offset.R: The log-likelihood improved by 0.0066.
> test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-Offset.R: Obtaining the responsible dyads.
> test-term-Offset.R: Evaluating the predictor and response matrix.
> test-term-Offset.R: Maximizing the pseudolikelihood.
> test-term-Offset.R: Finished MPLE.
> test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-Offset.R: Iteration 1 of at most 60:
> test-term-Offset.R: 1
> test-term-Offset.R: Optimizing with step length 1.0000.
> test-term-Offset.R: The log-likelihood improved by 0.0024.
> test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-Offset.R: Finished MCMLE.
> test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
Saving _problems/test-term-Offset-21.R
Saving _problems/test-term-Offset-25.R
> test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
> test-term-b12nodematch.R: In term 'b1nodematch' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: In term 'asymmetric' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Observed statistic(s) b1dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: In term 'b1factor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) ideg7+.homophily.group and ideg8+.homophily.group are at their smallest attainable values. Their coefficie
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: nts will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: In term 'b1twostar' in package 'ergm': Argument 'base' has been superseded by 'levels2', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-directed.R: Observed statistic(s) gwodeg.fixed.0 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Observed statistic(s) b2dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: In term 'nodeifactor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) odeg7+ and odeg8+ are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Observed statistic(s) odeg6+.homophily.group, odeg7+.homophily.group, and odeg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Finished MPLE.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-bipartite.R: Obtaining the responsible dyads.
> test-term-bipartite.R: Evaluating the predictor and response matrix.
> test-term-bipartite.R: Maximizing the pseudolikelihood.
> test-term-bipartite.R: Finished MPLE.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-directed.R: Obtaining the responsible dyads.
> test-term-directed.R: Evaluating the predictor and response matrix.
> test-term-directed.R: Maximizing the pseudolikelihood.
> test-term-directed.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Observed statistic(s) edgecov.YearsTrusted are at their greatest attainable values. Their coefficients will be fixed at +Inf.
> test-term-flexible.R: All terms are either offsets or extreme values. No optimization is performed.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-flexible.R: Obtaining the responsible dyads.
> test-term-flexible.R: Evaluating the predictor and response matrix.
> test-term-flexible.R: Maximizing the pseudolikelihood.
> test-term-flexible.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-mm.R: Note: Term 'mm(~Grade >= 10, levels = -1)' skipped because it contributes no statistics.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-mm.R: Obtaining the responsible dyads.
> test-term-mm.R: Evaluating the predictor and response matrix.
> test-term-mm.R: Maximizing the pseudolikelihood.
> test-term-mm.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: 1 Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0013.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: Evaluating log-likelihood at the estimate.
> test-term-options.R: Fitting the dyad-independent submodel...
> test-term-options.R: Bridging between the dyad-independent submodel and the full model...
> test-term-options.R: Setting up bridge sampling...
> test-term-options.R: Using 16 bridges: 1
> test-term-options.R: 2 3
> test-term-options.R: 4
> test-term-options.R: 5 6 7
> test-term-options.R: 8
> test-term-options.R: 9
> test-term-options.R: 10
> test-term-options.R: 11
> test-term-options.R: 12
> test-term-options.R: 13
> test-term-options.R: 14
> test-term-options.R: 15
> test-term-options.R: 16 .
> test-term-options.R: Bridging finished.
> test-term-options.R:
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-options.R: Obtaining the responsible dyads.
> test-term-options.R: Evaluating the predictor and response matrix.
> test-term-options.R: Maximizing the pseudolikelihood.
> test-term-options.R: Finished MPLE.
> test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-term-options.R: Iteration 1 of at most 60:
> test-term-options.R: 1 Optimizing with step length 1.0000.
> test-term-options.R: The log-likelihood improved by 0.0003.
> test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-term-options.R: Finished MCMLE.
> test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check
> test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-undirected.R: Obtaining the responsible dyads.
> test-term-undirected.R: Evaluating the predictor and response matrix.
> test-term-undirected.R: Maximizing the pseudolikelihood.
> test-term-undirected.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-term-gw-sp.R: Obtaining the responsible dyads.
> test-term-gw-sp.R: Evaluating the predictor and response matrix.
> test-term-gw-sp.R: Maximizing the pseudolikelihood.
> test-term-gw-sp.R: Finished MPLE.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
> test-u-function.R: Best valid proposal 'DiscUnif2' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-u-function.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'.
> test-valued-sim.R: mean=1, var=4, corr=0.3
> test-valued-sim.R: eta=(0.192307692307692,0.0824175824175824,0.362637362637363)
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated mean (stats only):0.9964445
> test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: Simulated means (target=1):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 1.0034478 0.9683903
> test-valued-sim.R: [2,] 1.0302079 NA 0.8923730
> test-valued-sim.R: [3,] 0.6870641 0.7628159 NA
> test-valued-sim.R: Simulated vars (target=4):
> test-valued-sim.R: [,1] [,2] [,3]
> test-valued-sim.R: [1,] NA 3.863045 4.034701
> test-valued-sim.R: [2,] 3.944388 NA 3.888997
> test-valued-sim.R: [3,] 3.740890 4.202553 NA
> test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3):
> test-valued-sim.R: [1] 0.2781206 0.2324470 0.3420405
> test-valued-sim.R: ==== output='stats', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.380183
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=2.8858
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='stats', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R: ==== output='network', coef=0
> test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-sim.R:
> test-valued-sim.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-sim.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-sim.R: * 'citation("ergm.count")' for citation information
> test-valued-sim.R: * 'https://statnet.org' for help, support, and other information
> test-valued-sim.R:
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
> test-valued-terms.R:
> test-valued-terms.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
> test-valued-terms.R: * 'news(package="ergm.count")' for changes since last version
> test-valued-terms.R: * 'citation("ergm.count")' for citation information
> test-valued-terms.R: * 'https://statnet.org' for help, support, and other information
> test-valued-terms.R:
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• empty test (1):
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-term-Offset.R:19:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `off <- ergm(nw ~ edges + offset(triangle), offset.coef = 0.1)` to throw a message.
── Failure ('test-term-Offset.R:23:3'): Estimation with Offset() operator works ──
Expected failure message to match regexp ".* did not throw the expected message.*".
Actual message:
x | Expected `Off <- ergm(nw ~ edges + Offset(~triangle, which = 1, coef = 0.1))` to throw a message.
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-linux-x86_64
Version: 4.10.1
Check: installed package size
Result: NOTE
installed size is 8.4Mb
sub-directories of 1Mb or more:
R 2.0Mb
doc 1.3Mb
help 1.0Mb
libs 3.3Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64
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