CRAN Package Check Results for Maintainer ‘Pavel N. Krivitsky <pavel at statnet.org>’

Last updated on 2026-02-15 07:51:18 CET.

Package	ERROR	WARN	NOTE	OK
ergm	4	1	3	6
ergm.count				14
ergm.ego				14
ergm.multi				14
ergm.rank				14
latentnet			3	11
piecemeal				14
rle	1			13
statnet.common			14
tergm			5	9

Package ergm

Current CRAN status: ERROR: 4, WARN: 1, NOTE: 3, OK: 6

Version: 4.11.0
Flags: --no-vignettes
Check: tests
Result: ERROR Running ‘requireNamespaceTest.R’ [3s/3s] Running ‘testthat.R’ [367s/202s] 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(statnet.common) Attaching package: 'statnet.common' The following objects are masked from 'package:base': attr, order, replace > library(ergm) Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.11.0 (2025-12-22), 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. Attaching package: 'ergm' The following object is masked from 'package:statnet.common': snctrl > > 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 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 > test-basis.R: 3 > test-basis.R: 4 5 > test-basis.R: 6 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 11 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-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 > test-basis.R: 2 3 > test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: 4 5 > test-basis.R: 6 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 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 > 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 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-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: 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-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: 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: 1 > test-bridge-target.stats.R: 2 > test-bridge-target.stats.R: 3 > test-bridge-target.stats.R: 4 5 > test-bridge-target.stats.R: 6 7 > test-bridge-target.stats.R: 8 > test-bridge-target.stats.R: 9 10 11 12 > test-bridge-target.stats.R: 13 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: > 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: 1 > 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: 2 > 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 5 6 > test-basis.R: 7 > test-bridge-target.stats.R: 3 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 12 > test-basis.R: 13 14 > test-basis.R: 15 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-bridge-target.stats.R: 4 > 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-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-bridge-target.stats.R: 5 > 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 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: 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-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: 13 > test-bridge-target.stats.R: 14 > test-bridge-target.stats.R: 15 > test-bridge-target.stats.R: 16 > test-basis.R: 1 Optimizing with step length 1.0000. > 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: 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: 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: > test-basis.R: 1 > test-bridge-target.stats.R: Using 16 bridges: 1 > test-basis.R: 2 3 > test-basis.R: 4 5 6 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 10 > test-basis.R: 11 > test-bridge-target.stats.R: 2 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 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: 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-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 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-bridge-target.stats.R: 4 > 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: 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-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: 11 > test-bridge-target.stats.R: 12 > 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: 13 > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: Using 16 bridges: 1 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: 14 > test-basis.R: 8 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 13 14 > 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-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-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/RtmpOzSvFD/filea291749b2cca1_001.RData'. > 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-checkpointing.R: 1 > test-bridge-target.stats.R: 7 > 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/RtmpOzSvFD/filea291749b2cca1_002.RData'. > test-bridge-target.stats.R: 8 > test-bridge-target.stats.R: 9 > test-bridge-target.stats.R: 10 > test-checkpointing.R: 1 > test-bridge-target.stats.R: 11 > 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: 12 > 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: 13 > test-checkpointing.R: Using 16 bridges: 1 > test-bridge-target.stats.R: 14 > test-checkpointing.R: 2 > test-checkpointing.R: 3 > test-bridge-target.stats.R: 15 > test-checkpointing.R: 4 5 6 > test-checkpointing.R: 7 > test-bridge-target.stats.R: 16 > test-checkpointing.R: 8 > test-checkpointing.R: 9 > test-bridge-target.stats.R: . > test-bridge-target.stats.R: Bridging finished. > test-checkpointing.R: 10 11 > test-bridge-target.stats.R: Fitting the dyad-independent submodel... > test-checkpointing.R: 12 > test-checkpointing.R: 13 > test-checkpointing.R: 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-checkpointing.R: 16 > test-checkpointing.R: . > test-bridge-target.stats.R: Using 16 bridges: 1 > 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/RtmpOzSvFD/filea291749b2cca1_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-bridge-target.stats.R: 5 > test-checkpointing.R: 1 Optimizing with step length 1.0000. > test-bridge-target.stats.R: 6 > 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-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 2 3 > test-checkpointing.R: 4 > test-bridge-target.stats.R: 8 > test-checkpointing.R: 5 > test-checkpointing.R: 6 > test-checkpointing.R: 7 8 > test-checkpointing.R: 9 > test-bridge-target.stats.R: 9 > test-checkpointing.R: 10 11 > test-checkpointing.R: 12 13 > test-checkpointing.R: 14 15 > test-checkpointing.R: 16 > test-bridge-target.stats.R: 10 > 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: 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-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 '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-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 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-constrain-dind.R: 1 > test-constrain-dind.R: Optimizing with step length 1.0000. > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > 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 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 10 > test-constrain-dind.R: 11 > test-constrain-dind.R: 12 > test-constrain-dind.R: 13 14 > test-constrain-dind.R: 15 > test-constrain-dind.R: 16 . > 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-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: 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: 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-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 > 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-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 3 > test-drop.R: 4 > test-drop.R: 5 > test-drop.R: 6 > test-drop.R: 7 8 > test-drop.R: 9 > 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: 10 11 > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-drop.R: 12 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-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 0.0068. > 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-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: Using 16 bridges: 1 > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-drop.R: 2 3 > test-drop.R: 4 > test-drop.R: 5 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 > 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-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: Initializing unconstrained Metropolis-Hastings proposal: > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > 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 free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -5.244530e-01 2.592560e-01 -3.147987e-01 -9.254589e-01 2.322348e-12 > 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-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-drop.R: New interval = 512. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -3.399177 -1.395062 -4.641975 -3.370370 2.170829 > test-drop.R: Starting MCMLE Optimization... > test-drop.R: 1 Optimizing with step length 0.9524. > 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-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-drop.R: Estimating equations are not within tolerance region. > test-drop.R: > test-drop.R: Iteration 2 of at most 3 with free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -7.679134e-01 3.298974e-01 -4.787791e-01 -1.324882e+00 9.046976e-10 > test-drop.R: Starting unconstrained MCMC... > 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-drop.R: Back from unconstrained MCMC. > test-drop.R: New interval = 256. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -0.2427984 0.4115226 -0.1934156 -0.5020576 -0.2889661 > test-drop.R: Starting MCMLE Optimization... > test-drop.R: 1 Optimizing with step length 0.9524. > test-drop.R: Using lognormal metric (see control.ergm function). > test-drop.R: Optimizing loglikelihood > test-constraints.R: 8 > 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 free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -1.0610706 1.2599949 -0.5094541 -1.4594441 0.1742152 > test-drop.R: Starting unconstrained MCMC... > 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-drop.R: Back from unconstrained MCMC. > test-constraints.R: 16 > test-drop.R: New interval = 128. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -0.4855967 -0.3621399 -0.6090535 -0.5209768 0.5709312 > test-drop.R: Starting MCMLE Optimization... > test-drop.R: 1 Optimizing with step length 0.9524. > 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 0.0416. > test-drop.R: Distance from origin on tolerance region scale: 0.9245623 (previously Inf). > test-constraints.R: . > 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 = 10.41146, with 4 free parameter(s) and 238.9876 degrees of freedom. > test-drop.R: Convergence test p-value: 0.0387. 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-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: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms... > test-constraints.R: > test-drop.R: Fitting the dyad-independent submodel... > 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 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-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=[-2.0110138, -Inf, 1.4621780,-0.4923718, -Inf,-0.9527669, 0.1279654, 0.0000000]. > test-drop.R: Running theta=[-2.0515328, -Inf, 1.3678439,-0.4606058, -Inf,-0.8912980, 0.1197096, 0.0000000]. > test-constraints.R: Iteration 1 of at most 60: > test-drop.R: Running theta=[-2.0920517, -Inf, 1.2735099,-0.4288399, -Inf,-0.8298292, 0.1114537, 0.0000000]. > test-drop.R: Running theta=[-2.1325706, -Inf, 1.1791758,-0.3970740, -Inf,-0.7683604, 0.1031979, 0.0000000]. > test-drop.R: Running theta=[-2.17308956, -Inf, 1.08484175,-0.36530808, -Inf,-0.70689155, 0.09494207, 0.00000000]. > test-drop.R: Running theta=[-2.21360850, -Inf, 0.99050769,-0.33354216, -Inf,-0.64542272, 0.08668623, 0.00000000]. > test-drop.R: Running theta=[-2.2541274, -Inf, 0.8961736,-0.3017762, -Inf,-0.5839539, 0.0784304, 0.0000000]. > test-drop.R: Running theta=[-2.29464637, -Inf, 0.80183956,-0.27001032, -Inf,-0.52248506, 0.07017457, 0.00000000]. > test-drop.R: Running theta=[-2.33516531, -Inf, 0.70750549,-0.23824440, -Inf,-0.46101623, 0.06191874, 0.00000000]. > test-drop.R: Running theta=[-2.37568425, -Inf, 0.61317143,-0.20647848, -Inf,-0.39954740, 0.05366291, 0.00000000]. > test-drop.R: Running theta=[-2.41620318, -Inf, 0.51883736,-0.17471256, -Inf,-0.33807857, 0.04540708, 0.00000000]. > test-drop.R: Running theta=[-2.45672212, -Inf, 0.42450329,-0.14294664, -Inf,-0.27660974, 0.03715124, 0.00000000]. > test-drop.R: Running theta=[-2.49724105, -Inf, 0.33016923,-0.11118072, -Inf,-0.21514091, 0.02889541, 0.00000000]. > test-drop.R: Running theta=[-2.53775999, -Inf, 0.23583516,-0.07941480, -Inf,-0.15367208, 0.02063958, 0.00000000]. > test-drop.R: Running theta=[-2.57827893, -Inf, 0.14150110,-0.04764888, -Inf,-0.09220325, 0.01238375, 0.00000000]. > test-drop.R: Running theta=[-2.618797861, -Inf, 0.047167033,-0.015882960, -Inf,-0.030734415, 0.004127916, 0.000000000]. > 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: 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-constraints.R: Using 16 bridges: 1 2 > test-constraints.R: 3 > test-constraints.R: 4 > test-constraints.R: 5 > test-constraints.R: 6 > test-constraints.R: 7 > 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: 8 > test-constraints.R: 9 > test-constraints.R: 10 > test-constraints.R: 11 > test-constraints.R: 12 13 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-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > 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-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 50 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: I(formula) (valued) > test-ergm-term-doc.R: Substitute a formula into the model formula > 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: cdf(min = NULL, max = NULL, by = NULL, margin = 0.1, nmax = 100) (valued) > test-ergm-term-doc.R: Empirical cumulative distribution function (unnormalized) of > test-ergm-term-doc.R: the network's dyad values > 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 17 matching ergm terms: > test-ergm-term-doc.R: ChangeStats(fix, check_dind = TRUE) > test-ergm-term-doc.R: Specified statistics must remain constant > test-ergm-term-doc.R: > 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 changestats > 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-ergmMPLE.R: Starting maximum pseudolikelihood estimation (MPLE): > test-ergmMPLE.R: Obtaining the responsible dyads. > test-ergmMPLE.R: Evaluating the predictor and response matrix. > test-ergm.bridge.llr.R: 3 > 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: 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-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: 10 > test-gflomiss.R: Finished MPLE. > test-gflomiss.R: Evaluating log-likelihood at the estimate. > test-gflomiss.R: > test-ergm.bridge.llr.R: 11 > 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: 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: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gflomiss.R: Iteration 1 of at most 60: > 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-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-ergm.bridge.llr.R: 7 > test-gflomiss.R: The log-likelihood improved by 0.0067. > 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. 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-gflomiss.R: Using 16 bridges: 1 > test-gflomiss.R: 2 > test-gflomiss.R: 3 > test-ergm.bridge.llr.R: 8 > test-gflomiss.R: 4 > test-gflomiss.R: 5 > test-ergm.bridge.llr.R: 9 > test-gflomiss.R: 6 > test-gflomiss.R: 7 > test-ergm.bridge.llr.R: 10 > test-gflomiss.R: 8 > test-gflomiss.R: 9 > test-gflomiss.R: 10 > test-ergm.bridge.llr.R: 11 > test-gflomiss.R: 11 > test-gflomiss.R: 12 > test-ergm.bridge.llr.R: 12 > test-gflomiss.R: 13 > test-gflomiss.R: 14 > test-gflomiss.R: 15 > test-ergm.bridge.llr.R: 13 > 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 > 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: 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-gflomiss.R: 1 Optimizing with step length 1.0000. > test-gflomiss.R: The log-likelihood improved by 0.0050. > test-ergm.bridge.llr.R: Using 16 bridges: 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-ergm.bridge.llr.R: 3 > test-gflomiss.R: Using 16 bridges: 1 > test-ergm.bridge.llr.R: 4 > test-gflomiss.R: 2 > test-gflomiss.R: 3 > test-ergm.bridge.llr.R: 5 > test-gflomiss.R: 4 5 > test-gflomiss.R: 6 7 > test-ergm.bridge.llr.R: 6 > test-gflomiss.R: 8 > test-gflomiss.R: 9 > test-gflomiss.R: 10 > test-gflomiss.R: 11 > test-ergm.bridge.llr.R: 7 > test-gflomiss.R: 12 > test-gflomiss.R: 13 14 > test-ergm.bridge.llr.R: 8 > 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: 9 > test-ergm.bridge.llr.R: 10 > 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: 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-gmonkmiss.R: Maximizing the pseudolikelihood. > test-gmonkmiss.R: Finished MPLE. > 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-ergm.bridge.llr.R: 13 > 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-ergm.bridge.llr.R: Setting up bridge sampling... > test-ergm.bridge.llr.R: Using 16 bridges: 1 > 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: 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 0.0078. > 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-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-gmonkmiss.R: 1 > test-gmonkmiss.R: The log-likelihood improved by 0.1501. > test-gmonkmiss.R: Iteration 3 of at most 60: > test-ergm.bridge.llr.R: 6 > 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 > 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 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-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: 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-gmonkmiss.R: 1 > 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-ergm.bridge.llr.R: 16 > 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-gmonkmiss.R: Maximizing the pseudolikelihood. > test-gmonkmiss.R: Finished MPLE. > test-ergm.bridge.llr.R: . > 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-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: 5 > test-ergm.bridge.llr.R: 6 > test-gmonkmiss.R: 1 Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 7 > 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: 8 > 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: 9 > test-ergm.bridge.llr.R: 10 > 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: 11 > test-ergm.bridge.llr.R: 12 > test-gof.R: Starting maximum pseudolikelihood estimation (MPLE): > test-ergm.bridge.llr.R: 13 > 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: 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-gof.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-gof.R: Starting contrastive divergence estimation via CD-MCMLE: > test-gof.R: Iteration 1 of at most 60: > test-gof.R: Convergence test P-value:1.8e-266 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 1.541. > test-gof.R: Iteration 2 of at most 60: > test-gof.R: Convergence test P-value:2e-184 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 1.461. > test-gof.R: Iteration 3 of at most 60: > test-gof.R: Convergence test P-value:1.8e-38 > test-gof.R: 1 > test-ergm.bridge.llr.R: 2 > test-gof.R: The log-likelihood improved by 0.1713. > test-gof.R: Iteration 4 of at most 60: > test-gof.R: Convergence test P-value:5.5e-05 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.02153. > test-gof.R: Iteration 5 of at most 60: > test-gof.R: Convergence test P-value:3.3e-01 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.00457. > test-gof.R: Iteration 6 of at most 60: > test-gof.R: Convergence test P-value:4.4e-02 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.009062. > test-gof.R: Iteration 7 of at most 60: > test-gof.R: Convergence test P-value:4.6e-01 > test-gof.R: 1 > test-ergm.bridge.llr.R: 3 > test-gof.R: The log-likelihood improved by 0.003667. > test-gof.R: Iteration 8 of at most 60: > test-gof.R: Convergence test P-value:8.7e-01 > test-gof.R: Convergence detected. Stopping. > test-gof.R: 1 The log-likelihood improved by 0.001439. > test-gof.R: Finished CD. > test-gof.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.R: Iteration 1 of at most 2: > 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-gof.R: 1 > test-ergm.bridge.llr.R: 9 > test-gof.R: Optimizing with step length 1.0000. > test-gof.R: The log-likelihood improved by 0.2102. > test-gof.R: Estimating equations are not within tolerance region. > test-gof.R: Iteration 2 of at most 2: > 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-gof.R: 1 > test-gof.R: Optimizing with step length 1.0000. > test-gof.R: The log-likelihood improved by 0.0698. > test-ergm.bridge.llr.R: 14 > test-gof.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-gof.R: Finished MCMLE. > test-gof.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.R: for degeneracy, use the mcmc.diagnostics() function. > test-gof.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-ergm.bridge.llr.R: 15 > test-gof.R: > test-gof.R: Goodness-of-fit for > test-ergm.bridge.llr.R: 16 > test-gof.R: model statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: sum 168 124 167.55 196 0.98 > test-gof.R: nonzero 88 68 88.02 106 1.00 > test-gof.R: nodematch.sum.group.Loyal 49 33 51.56 74 0.78 > test-gof.R: nodematch.sum.group.Outcasts 20 9 19.55 32 0.98 > test-gof.R: nodematch.sum.group.Turks 59 39 58.04 74 0.96 > test-gof.R: > test-gof.R: Goodness-of-fit for cumulative distribution function > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: 0 218 200 217.98 238 1.00 > test-gof.R: 1 256 241 253.05 272 0.56 > test-gof.R: 2 276 271 279.42 290 0.48 > test-gof.R: 3 306 306 306.00 306 1.00 > test-gof.R: 4 306 306 306.00 306 1.00 > test-gof.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-ergm.bridge.llr.R: . > test-ergm.bridge.llr.R: Bridging finished. > test-gof.R: > test-gof.R: Goodness-of-fit for model statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: sum 168 131 163.65 201 0.76 > test-gof.R: nonzero 88 70 86.60 106 0.82 > test-gof.R: nodematch.sum.group.Loyal 49 25 48.56 68 0.94 > test-gof.R: nodematch.sum.group.Outcasts 20 12 19.57 31 0.88 > test-gof.R: nodematch.sum.group.Turks 59 31 57.33 77 0.88 > test-gof.R: > test-gof.R: Goodness-of-fit for user statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: atmost.0 218 200 219.40 236 0.82 > test-gof.R: atmost.1 256 239 254.73 265 0.90 > test-gof.R: atmost.2 276 272 280.22 288 0.34 > test-gof.R: atmost.3 306 306 306.00 306 1.00 > test-gof.R: atmost.4 306 306 306.00 306 1.00 > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > 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 > test-metrics.R: 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 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 > test-miss-dep.R: 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-miss-dep.R: The log-likelihood improved by 1.824. > 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 0.0037. > 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-miss-dep.R: Convergence test P-value:1.5e-23 > test-miss-dep.R: 1 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 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 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 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 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-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: 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 2.2132. > 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 0.0377. > 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 0.0017. > 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-miss-dep.R: Using 16 bridges: > test-miss-dep.R: 1 > test-miss-dep.R: 2 > test-metrics.R: 1 Optimizing with step length 0.4158. > test-miss-dep.R: 3 > 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: 4 > test-miss-dep.R: 5 6 7 > test-miss-dep.R: 8 > test-miss-dep.R: 9 > test-metrics.R: 1 Optimizing with step length 0.4804. > test-miss-dep.R: 10 > 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: 11 > test-miss-dep.R: 12 > test-miss-dep.R: 13 > test-metrics.R: 1 Optimizing with step length 1.0000. > test-miss-dep.R: 14 > test-miss-dep.R: 15 > 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: 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=20, density=0.1, missing=0.1 > 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: 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-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-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 > 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 > 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-metrics.R: 3 4 5 6 7 > test-metrics.R: 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: 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-metrics.R: 1 Optimizing with step length 1.0000. > 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 2.8651. > 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: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 > 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 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 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 0.3360. > 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: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-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > 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: The log-likelihood improved by 0.0827. > 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 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-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 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: 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 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 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by 1.711. > test-miss.CD.R: Iteration 2 of at most 60: > test-metrics.R: 1 2 3 4 5 6 7 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:2.9e-233 > test-miss.CD.R: 1 2 > test-miss.CD.R: The log-likelihood improved by 1.753. > test-miss.CD.R: Iteration 3 of at most 60: > test-miss.CD.R: Convergence test P-value:4.4e-193 > 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 4 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 0.5218. > 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:1.5e-199 > 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:2e-201 > test-miss.CD.R: 1 2 > test-metrics.R: 1 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: 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:1.3e-208 > 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-miss.CD.R: Convergence test P-value:4.7e-207 > 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 8 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:2.7e-202 > test-miss.CD.R: 1 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:4.1e-205 > test-miss.CD.R: 1 2 > test-metrics.R: 1 Optimizing with step length 1.0000. > 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 10 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:1.4e-210 > 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-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:5.2e-187 > 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:1.2e-199 > 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-miss.CD.R: Convergence test P-value:6.6e-211 > 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: 1 2 3 4 > test-metrics.R: 5 6 7 8 9 10 11 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:2.2e-203 > 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:5.2e-197 > test-miss.CD.R: 1 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 > test-metrics.R: 2 3 4 5 > test-metrics.R: 6 > test-metrics.R: 7 8 > test-metrics.R: 9 > 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: Convergence test P-value:1e-201 > 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-miss.CD.R: Convergence test P-value:2.1e-202 > 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 > 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: Convergence test P-value:6.3e-210 > test-miss.CD.R: 1 2 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:1.3e-213 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-metrics.R: 1 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 20 of at most 60: > test-metrics.R: 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:2.6e-202 > test-miss.CD.R: 1 > test-miss.CD.R: 2 The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 21 of at most 60: > test-miss.R: n=20, density=0.1, missing=0.05 > test-miss.CD.R: Convergence test P-value:5.5e-202 > 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 22 of at most 60: > test-miss.CD.R: Convergence test P-value:7.2e-205 > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 23 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.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:3.5e-207 > test-miss.R: 'ergm:MH_SPDyad'. > test-miss.R: Initializing model... > test-miss.R: Model initialized. > test-miss.R: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.CD.R: 1 2 > test-miss.R: Model reinitialized. > 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.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Imputing 3 edges at random. > test-miss.CD.R: Iteration 24 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 41 edges. > test-miss.R: > test-miss.R: Iteration 1 of at most 60 with free parameter vector: > test-miss.R: edges > test-miss.R: -1.118156 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:1.2e-197 > test-miss.CD.R: 1 2 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:4e-193 > 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 26 of at most 60: > test-miss.CD.R: Convergence test P-value:1.8e-212 > test-miss.CD.R: 1 > test-miss.CD.R: 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.374066 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:8e-208 > test-miss.CD.R: 1 2 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:1.4e-198 > 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-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:2.3e-204 > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.647302 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:2.5e-205 > 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:9.4e-205 > 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.CD.R: Convergence test P-value:2.8e-202 > 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 = 128. > test-miss.R: New constrained interval = 128. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -17.50766 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.95412 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:3.3e-209 > 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 34 of at most 60: > test-miss.R: Back from constrained MCMC. > test-miss.CD.R: Convergence test P-value:2.8e-201 > test-miss.CD.R: 1 2 > test-miss.R: New interval = 64. > test-miss.R: New constrained interval = 64. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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.CD.R: Convergence test P-value:5.8e-206 > test-miss.R: Back from unconstrained MCMC. > 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 36 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: Estimated gradient of the log-likelihood: > 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.CD.R: Convergence test P-value:1.3e-192 > test-miss.CD.R: 1 2 > 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: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 37 of at most 60: > test-miss.R: Fitting the dyad-independent submodel... > test-miss.CD.R: Convergence test P-value:5.1e-199 > 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: 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:1.7e-208 > 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 39 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.CD.R: Convergence test P-value:1.2e-197 > test-miss.CD.R: 1 2 > test-miss.R: Running theta=[-2.126341, 0.000000]. > test-miss.R: Running theta=[-2.125378, 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=[-2.124415, 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.R: Running theta=[-2.120563, 0.000000]. > test-miss.CD.R: Convergence test P-value:1.1e-205 > test-miss.CD.R: 1 2 > 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.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 41 of at most 60: > test-miss.R: Running theta=[-2.119968, 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:1.2e-204 > test-miss.R: Running theta=[-2.12382, 0.00000]. > test-miss.R: Running theta=[-2.124783, 0.000000]. > test-miss.CD.R: 1 > test-miss.CD.R: 2 > 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 42 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.CD.R: Convergence test P-value:8.4e-196 > test-miss.R: Running theta=[-2.131523, 0.000000]. > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.132486, 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=[-2.133449, 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.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.CD.R: Convergence test P-value:7.5e-206 > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 44 of at most 60: > 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.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.CD.R: Convergence test P-value:1.9e-197 > test-miss.CD.R: 1 2 > test-miss.R: Running theta=[-2.121299, 0.000000]. > test-miss.R: Running theta=[-2.120336, 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=[-2.119373, 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:2.8e-211 > test-miss.CD.R: 1 > test-miss.R: Running theta=[-2.12263, 0.00000]. > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.123593, 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=[-2.124555, 0.000000]. > 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.CD.R: Convergence test P-value:6.5e-203 > test-miss.CD.R: 1 > test-miss.CD.R: 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 47 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.CD.R: Convergence test P-value:4.7e-204 > test-miss.CD.R: 1 > test-miss.R: Running theta=[-2.130701, 0.000000]. > test-miss.R: Running theta=[-2.129738, 0.000000]. > test-miss.CD.R: 2 > 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 48 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:5.7e-209 > test-miss.CD.R: 1 2 > test-miss.R: Running theta=[-2.122997, 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=[-2.122035, 0.000000]. > test-miss.R: Running theta=[-2.121072, 0.000000]. > 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.CD.R: Convergence test P-value:4e-201 > test-miss.CD.R: 1 2 > 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 50 of at most 60: > test-miss.CD.R: Convergence test P-value:9.5e-218 > 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:5.7e-193 > 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.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:2.5e-206 > test-miss.CD.R: 1 2 > 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: 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:6e-198 > 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: Correct estimate = > test-miss.CD.R: Convergence test P-value:1.5e-204 > 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: -1.663142 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.CD.R: Convergence test P-value:2.9e-195 > test-miss.CD.R: 1 > 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: 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: 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.CD.R: Convergence test P-value:6.8e-200 > test-miss.CD.R: 1 2 > test-miss.R: Model initialized. > test-miss.R: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.R: Model reinitialized. > 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: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 57 of at most 60: > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -0.6631421 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:8e-207 > test-miss.CD.R: 1 > test-miss.CD.R: 2 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:3.6e-215 > test-miss.CD.R: 1 > test-miss.CD.R: 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:1.6e-199 > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.146333 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:6.1e-198 > 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... Saving _problems/test-miss.CD-76.R > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 256. > test-miss.R: New constrained interval = 128. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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-miss.R: Back from unconstrained MCMC. > test-mple-cov.R: Maximizing the pseudolikelihood. > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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. > test-miss.R: 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: MPLE estimate = -3.157 with log-likelihood -8.355963 OK. > 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: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.R: Model reinitialized. > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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 > test-miss.R: 4 > test-miss.R: 5 6 7 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 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-miss.R: 1 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: 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-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks. > 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 > 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: 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 > 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-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-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: 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: 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 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 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 0.0297. > 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 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1793. > 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 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 3 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. > 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 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-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: 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 Optimizing with step length 1.0000. > 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. 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: 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-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-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 > 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: 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-nodrop.R: Maximizing the pseudolikelihood. > test-nodrop.R: Finished MPLE. > test-networkLite.R: 1 Optimizing with step length 1.0000. > test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (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: 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 > 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: 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-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 0.0176. > 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: 0.0247. 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-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 0.0103. > 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 > 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: 1 > test-networkLite.R: 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. > 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-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-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: 2 3 4 5 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 10 11 12 13 > test-networkLite.R: 14 15 16 17 18 19 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-nonident-test.R: 1 Optimizing with step length 0.8147. > test-networkLite.R: 1 2 3 4 5 6 > 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: 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 0.0094. > 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: 0.0010. 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: 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: 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-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 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-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-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-networkLite.R: 1 Optimizing with step length 1.0000. > 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: 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-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 0.0419. > 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: 0.0085. 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: > 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 2 > test-networkLite.R: 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-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: 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: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-offsets.R: Iteration 1 of at most 2: > 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-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: 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-offsets.R: 1 Optimizing with step length 1.0000. > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0078. > test-offsets.R: The log-likelihood improved by 0.0061. > 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: 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-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: > test-offsets.R: 1 > test-offsets.R: 2 > test-offsets.R: 3 > test-offsets.R: 4 > test-offsets.R: 5 > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-offsets.R: 6 7 > test-offsets.R: 8 > test-offsets.R: 9 > test-offsets.R: 10 > 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-offsets.R: 11 > 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: 12 > test-offsets.R: 13 > test-offsets.R: 14 > test-offsets.R: 15 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: 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: 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 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 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 > 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-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 > 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: 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-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-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-offsets.R: 7 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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-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-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-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-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-scoping.R: Using 16 bridges: 1 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 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-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-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 > test-scoping.R: 3 > test-scoping.R: 4 5 > test-scoping.R: 6 > test-scoping.R: 7 > test-scoping.R: 8 9 > test-scoping.R: 10 11 12 13 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-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 > test-shrink-into-CH.R: 9 > test-shrink-into-CH.R: 10 > test-shrink-into-CH.R: 11 12 13 > test-shrink-into-CH.R: 14 > test-shrink-into-CH.R: 15 > test-shrink-into-CH.R: 16 17 18 > test-shrink-into-CH.R: 19 > test-shrink-into-CH.R: 20 > test-shrink-into-CH.R: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 > test-shrink-into-CH.R: 18 > test-shrink-into-CH.R: 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: 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-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-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 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-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-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-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. 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-stocapprox.R: 1 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: nonzero transitiveweights.min.max.min > test-stocapprox.R: -1.743217 0.112619 > test-stocapprox.R: Starting burnin of 16384 steps > 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 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: 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-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-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 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 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 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: * '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. 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-target-offset.R: 10 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 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-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 0.7240. > 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 0.6386. > 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-target-offset.R: 1 Optimizing with step length 0.8376. > 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-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-target-offset.R: 8 9 > test-target-offset.R: 10 11 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-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 > test-target-offset.R: -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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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. 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'. > 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-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Obtaining the responsible dyads. > test-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-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-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-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-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-bipartite.R: Finished MPLE. > 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) 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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > 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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-directed.R: Starting 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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-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-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-flexible.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-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-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-flexible.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-flexible.R: Starting 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-flexible.R: Starting 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: Maximizing the pseudolikelihood. > test-term-flexible.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-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-flexible.R: Starting 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: 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: 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-flexible.R: Starting 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-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-flexible.R: Starting 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-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-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-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 > 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-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 5 > test-term-options.R: 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: . > 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-gw-sp.R: Finished MPLE. > 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-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-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-gw-sp.R: Finished 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-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-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: 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-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-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-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):0.9716704 > 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 0.9473966 1.0398479 > test-valued-sim.R: [2,] 0.9923737 NA 0.9471411 > test-valued-sim.R: [3,] 0.8264568 1.0305133 NA > test-valued-sim.R: Simulated vars (target=4): > test-valued-sim.R: [,1] [,2] [,3] > test-valued-sim.R: [1,] NA 3.865227 3.968847 > test-valued-sim.R: [2,] 3.818363 NA 3.945242 > test-valued-sim.R: [3,] 3.945397 3.950333 NA > test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3): > test-valued-sim.R: [1] 0.2798051 0.2854507 0.2927308 > 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 1 | WARN 0 | SKIP 2 | PASS 4296 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • check_ABI("ergm.count") is not TRUE (1): 'test-u-function.R:86:3' • empty test (1): ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-miss.CD.R:76:3'): curved+missing ───────────────────────────── Expected `abs(coef(cdfit)[1] - truth)/sqrt(cdfit$covar[1])` < 2. Actual comparison: 2.95 >= 2.00 Difference: 0.95 >= 0 [ FAIL 1 | WARN 0 | SKIP 2 | PASS 4296 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 4.11.0
Flags: --no-vignettes
Check: tests
Result: ERROR Running ‘requireNamespaceTest.R’ [2s/2s] Running ‘testthat.R’ [241s/132s] 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(statnet.common) Attaching package: 'statnet.common' The following objects are masked from 'package:base': attr, order, replace > library(ergm) Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.11.0 (2025-12-22), 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. Attaching package: 'ergm' The following object is masked from 'package:statnet.common': snctrl > > 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 > test-basis.R: 3 > test-basis.R: 4 5 > test-basis.R: 6 7 8 > test-basis.R: 9 > test-basis.R: 10 11 > test-basis.R: 12 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 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-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: 4 > test-basis.R: 5 6 7 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 12 > test-basis.R: 13 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-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 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 > test-basis.R: 4 5 6 7 8 9 > test-basis.R: 10 11 12 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: 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 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 > 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: 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 Optimizing with step length 1.0000. > test-bridge-target.stats.R: The log-likelihood improved by 0.0219. > test-basis.R: 1 Optimizing with step length 1.0000. > 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: 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-basis.R: 1 Optimizing with step length 1.0000. > test-bridge-target.stats.R: 3 4 5 > test-bridge-target.stats.R: 6 7 > 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: 8 9 10 > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-bridge-target.stats.R: 11 12 > test-basis.R: Using 16 bridges: 1 2 > test-bridge-target.stats.R: 13 14 15 > test-basis.R: 3 > test-basis.R: 4 5 > 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-basis.R: 6 7 8 > test-basis.R: 9 > test-bridge-target.stats.R: Fitting the dyad-independent submodel... > test-basis.R: 10 11 > test-basis.R: 12 > test-basis.R: 13 14 15 16 > 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: . > 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: Using 16 bridges: 1 > 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 > 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-bridge-target.stats.R: 2 > 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: 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-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: 8 > test-bridge-target.stats.R: 9 > test-basis.R: 1 Optimizing with step length 1.0000. > test-bridge-target.stats.R: 10 > test-basis.R: The log-likelihood improved by 0.0072. > 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. > 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: 12 > test-basis.R: Using 16 bridges: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 5 > test-bridge-target.stats.R: 13 > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 9 > test-bridge-target.stats.R: 14 > test-basis.R: 10 11 12 13 14 15 > test-bridge-target.stats.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: 16 > test-bridge-target.stats.R: . > test-bridge-target.stats.R: Bridging finished. > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-bridge-target.stats.R: Fitting the dyad-independent submodel... > 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 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-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: 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-bridge-target.stats.R: Using 16 bridges: 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: 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-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-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. > test-basis.R: 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: 9 > 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-bridge-target.stats.R: 10 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 12 13 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: 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-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/RtmpoR3vXF/file1b2f2064c83c05_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-bridge-target.stats.R: 3 > 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/RtmpoR3vXF/file1b2f2064c83c05_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 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-bridge-target.stats.R: 9 > 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 > test-checkpointing.R: 4 > test-checkpointing.R: 5 > test-checkpointing.R: 6 7 > test-checkpointing.R: 8 > test-checkpointing.R: 9 10 > test-checkpointing.R: 11 > test-checkpointing.R: 12 > test-checkpointing.R: 13 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-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/RtmpoR3vXF/file1b2f2064c83c05_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 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-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: 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-bridge-target.stats.R: 2 > test-checkpointing.R: Using 16 bridges: 1 2 > test-checkpointing.R: 3 4 > test-checkpointing.R: 5 > test-checkpointing.R: 6 > test-bridge-target.stats.R: 3 > test-checkpointing.R: 7 > test-checkpointing.R: 8 > test-checkpointing.R: 9 10 11 > test-bridge-target.stats.R: 4 > test-checkpointing.R: 12 13 > test-checkpointing.R: 14 > test-checkpointing.R: 15 > test-bridge-target.stats.R: 5 > test-checkpointing.R: 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-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 > 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 '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-dind.R: 8 > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > 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-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: > 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: > 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-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-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: 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: Fitting the dyad-independent submodel... > 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 > test-drop.R: 2 3 4 > test-drop.R: 5 6 > test-drop.R: 7 8 > test-drop.R: 9 > test-drop.R: 10 > test-drop.R: 11 > test-drop.R: 12 > test-drop.R: 13 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: 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 0.0018. > 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: 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: Fitting the dyad-independent submodel... > 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 4 > test-drop.R: 5 > test-drop.R: 6 > test-drop.R: 7 8 > test-drop.R: 9 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-drop.R: 11 > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-drop.R: 12 > test-drop.R: 13 > test-drop.R: 14 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: > 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 free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -5.244530e-01 2.592560e-01 -3.147987e-01 -9.254589e-01 2.322348e-12 > 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: 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-drop.R: Back from unconstrained MCMC. > test-constraints.R: Using 16 bridges: 1 > test-drop.R: New interval = 512. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -3.399177 -1.395062 -4.641975 -3.370370 2.170829 > 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-drop.R: Estimating equations are not within tolerance region. > test-drop.R: > test-drop.R: Iteration 2 of at most 3 with free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -7.679134e-01 3.298974e-01 -4.787791e-01 -1.324882e+00 9.046976e-10 > test-drop.R: Starting unconstrained MCMC... > 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-drop.R: Back from unconstrained MCMC. > test-constraints.R: 8 > test-drop.R: New interval = 256. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -0.2427984 0.4115226 -0.1934156 -0.5020576 -0.2889661 > 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 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 free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -1.0610706 1.2599949 -0.5094541 -1.4594441 0.1742152 > test-drop.R: Starting unconstrained MCMC... > 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-drop.R: Back from unconstrained MCMC. > test-drop.R: New interval = 128. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -0.4855967 -0.3621399 -0.6090535 -0.5209768 0.5709312 > 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-constraints.R: 15 > test-drop.R: Optimizing loglikelihood > test-drop.R: Starting MCMC s.e. computation. > test-drop.R: The log-likelihood improved by 0.0416. > test-drop.R: Distance from origin on tolerance region scale: 0.9245623 (previously Inf). > test-constraints.R: 16 > 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 = 10.41146, with 4 free parameter(s) and 238.9876 degrees of freedom. > test-drop.R: Convergence test p-value: 0.0387. 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. > test-drop.R: Initializing model to obtain the list of dyad-independent terms... > test-drop.R: Fitting the dyad-independent submodel... > 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: 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: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-drop.R: Model and proposals initialized. > test-drop.R: Using 16 bridges: Running theta=[-2.0110138, -Inf, 1.4621780,-0.4923718, -Inf,-0.9527669, 0.1279654, 0.0000000]. > test-drop.R: Running theta=[-2.0515328, -Inf, 1.3678439,-0.4606058, -Inf,-0.8912980, 0.1197096, 0.0000000]. > test-drop.R: Running theta=[-2.0920517, -Inf, 1.2735099,-0.4288399, -Inf,-0.8298292, 0.1114537, 0.0000000]. > 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-drop.R: Running theta=[-2.1325706, -Inf, 1.1791758,-0.3970740, -Inf,-0.7683604, 0.1031979, 0.0000000]. > test-drop.R: Running theta=[-2.17308956, -Inf, 1.08484175,-0.36530808, -Inf,-0.70689155, 0.09494207, 0.00000000]. > test-drop.R: Running theta=[-2.21360850, -Inf, 0.99050769,-0.33354216, -Inf,-0.64542272, 0.08668623, 0.00000000]. > 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-drop.R: Running theta=[-2.2541274, -Inf, 0.8961736,-0.3017762, -Inf,-0.5839539, 0.0784304, 0.0000000]. > test-drop.R: Running theta=[-2.29464637, -Inf, 0.80183956,-0.27001032, -Inf,-0.52248506, 0.07017457, 0.00000000]. > 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-drop.R: Running theta=[-2.33516531, -Inf, 0.70750549,-0.23824440, -Inf,-0.46101623, 0.06191874, 0.00000000]. > test-constraints.R: Iteration 1 of at most 60: > test-drop.R: Running theta=[-2.37568425, -Inf, 0.61317143,-0.20647848, -Inf,-0.39954740, 0.05366291, 0.00000000]. > test-drop.R: Running theta=[-2.41620318, -Inf, 0.51883736,-0.17471256, -Inf,-0.33807857, 0.04540708, 0.00000000]. > test-drop.R: Running theta=[-2.45672212, -Inf, 0.42450329,-0.14294664, -Inf,-0.27660974, 0.03715124, 0.00000000]. > test-drop.R: Running theta=[-2.49724105, -Inf, 0.33016923,-0.11118072, -Inf,-0.21514091, 0.02889541, 0.00000000]. > test-drop.R: Running theta=[-2.53775999, -Inf, 0.23583516,-0.07941480, -Inf,-0.15367208, 0.02063958, 0.00000000]. > test-drop.R: Running theta=[-2.57827893, -Inf, 0.14150110,-0.04764888, -Inf,-0.09220325, 0.01238375, 0.00000000]. > test-drop.R: Running theta=[-2.618797861, -Inf, 0.047167033,-0.015882960, -Inf,-0.030734415, 0.004127916, 0.000000000]. > 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: 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. > 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-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: 4 > test-constraints.R: 5 > 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: 6 > test-constraints.R: 7 > test-constraints.R: 8 > test-constraints.R: 9 10 > test-constraints.R: 11 > test-constraints.R: 12 > test-constraints.R: 13 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-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 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 50 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: I(formula) (valued) > test-ergm-term-doc.R: Substitute a formula into the model formula > 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: cdf(min = NULL, max = NULL, by = NULL, margin = 0.1, nmax = 100) (valued) > test-ergm-term-doc.R: Empirical cumulative distribution function (unnormalized) of > test-ergm-term-doc.R: the network's dyad values > 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 > test-ergm-term-doc.R: 17 matching ergm terms: > test-ergm-term-doc.R: ChangeStats(fix, check_dind = TRUE) > test-ergm-term-doc.R: Specified statistics must remain constant > test-ergm-term-doc.R: > 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 changestats > 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-ergm.bridge.llr.R: 5 > 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: 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-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-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-ergm.bridge.llr.R: 7 > test-gflomiss.R: 1 > test-gflomiss.R: Optimizing with step length 1.0000. > test-gflomiss.R: The log-likelihood improved by 0.0067. > test-ergm.bridge.llr.R: 8 > 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: 9 > 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: 10 > test-gflomiss.R: Using 16 bridges: 1 > test-gflomiss.R: 2 > test-gflomiss.R: 3 > 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-ergm.bridge.llr.R: 11 > test-gflomiss.R: 12 13 14 15 > test-ergm.bridge.llr.R: 12 > test-gflomiss.R: 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: 13 > test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gflomiss.R: Iteration 1 of at most 60: > test-ergm.bridge.llr.R: 14 > test-ergm.bridge.llr.R: 15 > test-gflomiss.R: 1 > test-gflomiss.R: Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 16 > test-gflomiss.R: The log-likelihood improved by 0.0050. > 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: . > test-ergm.bridge.llr.R: Fitting the dyad-independent submodel... > 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-gflomiss.R: Using 16 bridges: 1 > 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: 2 3 > test-gflomiss.R: 4 > test-ergm.bridge.llr.R: Using 16 bridges: 1 > test-gflomiss.R: 5 > test-gflomiss.R: 6 > test-ergm.bridge.llr.R: 2 > test-gflomiss.R: 7 8 9 10 > test-gflomiss.R: 11 12 > test-ergm.bridge.llr.R: 3 > test-gflomiss.R: 13 14 > test-gflomiss.R: 15 > test-gflomiss.R: 16 > test-ergm.bridge.llr.R: 4 > 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: 5 > test-ergm.bridge.llr.R: 6 > test-ergm.bridge.llr.R: 7 > 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: 8 > test-ergm.bridge.llr.R: 9 > test-ergm.bridge.llr.R: 10 > 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: 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-gmonkmiss.R: Maximizing the pseudolikelihood. > test-ergm.bridge.llr.R: 12 > 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: 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-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 0.6245. > test-gmonkmiss.R: Estimating equations are not within tolerance region. > test-gmonkmiss.R: Iteration 2 of at most 3: > test-ergm.bridge.llr.R: 2 > test-ergm.bridge.llr.R: 3 > test-gmonkmiss.R: 1 Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 4 > 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-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-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-ergm.bridge.llr.R: 6 > 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 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-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: 11 > 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-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: 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-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: . > 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 0.7035. > test-gmonkmiss.R: Estimating equations are not within tolerance region. > test-gmonkmiss.R: Iteration 2 of at most 3: > test-ergm.bridge.llr.R: 5 > test-ergm.bridge.llr.R: 6 > test-gmonkmiss.R: 1 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: 7 > 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: 8 > test-ergm.bridge.llr.R: 9 > test-ergm.bridge.llr.R: 10 > 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: 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-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-gof.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-gof.R: Starting contrastive divergence estimation via CD-MCMLE: > test-gof.R: Iteration 1 of at most 60: > test-gof.R: Convergence test P-value:1.8e-266 > test-gof.R: 1 The log-likelihood improved by 1.541. > test-gof.R: Iteration 2 of at most 60: > test-gof.R: Convergence test P-value:2e-184 > test-gof.R: 1 > 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: The log-likelihood improved by 1.461. > test-gof.R: Iteration 3 of at most 60: > test-gof.R: Convergence test P-value:1.8e-38 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.1713. > test-gof.R: Iteration 4 of at most 60: > test-gof.R: Convergence test P-value:5.5e-05 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.02153. > test-gof.R: Iteration 5 of at most 60: > test-ergm.bridge.llr.R: Using 16 bridges: 1 > test-gof.R: Convergence test P-value:3.3e-01 > test-gof.R: 1 The log-likelihood improved by 0.00457. > test-gof.R: Iteration 6 of at most 60: > test-gof.R: Convergence test P-value:4.4e-02 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.009062. > test-gof.R: Iteration 7 of at most 60: > test-gof.R: Convergence test P-value:4.6e-01 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.003667. > test-gof.R: Iteration 8 of at most 60: > test-gof.R: Convergence test P-value:8.7e-01 > test-gof.R: Convergence detected. Stopping. > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.001439. > test-gof.R: Finished CD. > test-gof.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.R: Iteration 1 of at most 2: > 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-gof.R: 1 Optimizing with step length 1.0000. > test-gof.R: The log-likelihood improved by 0.2102. > test-gof.R: Estimating equations are not within tolerance region. > test-gof.R: Iteration 2 of at most 2: > 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-gof.R: 1 > test-gof.R: Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 11 > test-gof.R: The log-likelihood improved by 0.0698. > test-gof.R: Convergence test p-value: 0.0024. > test-gof.R: Converged with 99% confidence. > test-gof.R: Finished MCMLE. > test-gof.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.R: for degeneracy, use the mcmc.diagnostics() function. > test-ergm.bridge.llr.R: 12 > test-gof.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-ergm.bridge.llr.R: 13 > test-gof.R: > test-gof.R: Goodness-of-fit for model statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: sum 168 124 167.55 196 0.98 > test-gof.R: nonzero 88 68 88.02 106 1.00 > test-gof.R: nodematch.sum.group.Loyal 49 33 51.56 74 0.78 > test-gof.R: nodematch.sum.group.Outcasts 20 9 19.55 32 0.98 > test-gof.R: nodematch.sum.group.Turks 59 39 58.04 74 0.96 > test-gof.R: > test-gof.R: Goodness-of-fit for cumulative distribution function > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: 0 218 200 217.98 238 1.00 > test-gof.R: 1 256 241 253.05 272 0.56 > test-gof.R: 2 276 271 279.42 290 0.48 > test-gof.R: 3 306 306 306.00 306 1.00 > test-gof.R: 4 306 306 306.00 306 1.00 > test-ergm.bridge.llr.R: 14 > test-gof.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-ergm.bridge.llr.R: 15 > test-gof.R: > test-gof.R: Goodness-of-fit for > test-gof.R: model statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: sum 168 131 163.65 201 0.76 > test-gof.R: nonzero 88 70 86.60 106 0.82 > test-gof.R: nodematch.sum.group.Loyal 49 25 48.56 68 0.94 > test-gof.R: nodematch.sum.group.Outcasts 20 12 19.57 31 0.88 > test-gof.R: nodematch.sum.group.Turks 59 31 57.33 77 0.88 > test-gof.R: > test-gof.R: Goodness-of-fit for user statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: atmost.0 218 200 219.40 236 0.82 > test-gof.R: atmost.1 256 239 254.73 265 0.90 > test-gof.R: atmost.2 276 272 280.22 288 0.34 > test-gof.R: atmost.3 306 306 306.00 306 1.00 > test-gof.R: atmost.4 306 306 306.00 306 1.00 > 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 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 > test-metrics.R: 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-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-metrics.R: 1 Optimizing with step length 1.0000. > 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-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: 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 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: 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. > 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-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: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 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 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 The log-likelihood improved by 0.002664. > test-miss-dep.R: Iteration 7 of at most 60: > test-metrics.R: 1 > test-miss-dep.R: Convergence test P-value:1.6e-01 > test-miss-dep.R: 1 The log-likelihood improved by 0.007694. > test-miss-dep.R: Iteration 8 of at most 60: > 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:1.9e-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 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-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-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-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: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > test-miss-dep.R: Using 16 bridges: 1 > test-miss-dep.R: 2 > test-miss-dep.R: 3 4 > test-miss-dep.R: 5 > 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: 6 7 > test-miss-dep.R: 8 > test-miss-dep.R: 9 > test-miss-dep.R: 10 > test-miss-dep.R: 11 12 > 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-dep.R: 13 > test-miss-dep.R: 14 15 > test-metrics.R: 1 Optimizing with step length 1.0000. > test-miss-dep.R: 16 > 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: . > 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=20, density=0.1, missing=0.1 > 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: 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-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: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 > 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-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-metrics.R: 1 2 3 4 5 6 7 > test-miss.CD.R: Convergence test P-value:7.9e-01 > test-miss.CD.R: Convergence detected. Stopping. > test-miss.CD.R: 1 > test-metrics.R: 8 9 10 11 Optimizing with step length 0.3934. > 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: 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-metrics.R: 1 Optimizing with step length 1.0000. > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE: > test-miss.CD.R: Iteration 1 of at most 60: > 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: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 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-metrics.R: 1 Optimizing with step length 1.0000. > 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: 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: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 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 0.0827. > 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-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-miss.CD.R: The log-likelihood improved by 0.5854. > test-miss.CD.R: Iteration 2 of at most 60: > test-metrics.R: Iteration 1 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 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 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-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: 2 3 > test-metrics.R: 4 5 > test-metrics.R: 6 > test-metrics.R: 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: 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 1.711. > test-miss.CD.R: Iteration 2 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 10 11 12 Optimizing with step length 0.5218. > test-miss.CD.R: Convergence test P-value:2.9e-233 > test-miss.CD.R: 1 > 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: 2 > test-miss.CD.R: The log-likelihood improved by 1.753. > test-miss.CD.R: Iteration 3 of at most 60: > test-miss.CD.R: Convergence test P-value:4.4e-193 > test-miss.CD.R: 1 2 > test-metrics.R: 1 > test-metrics.R: 2 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 < 0.0001. > test-miss.CD.R: Iteration 4 of at most 60: > test-miss.CD.R: Convergence test P-value:1.5e-199 > 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-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:2e-201 > 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:1.3e-208 > 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 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:4.7e-207 > 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:2.7e-202 > 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 9 of at most 60: > test-miss.CD.R: Convergence test P-value:4.1e-205 > 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:1.4e-210 > 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:5.2e-187 > test-miss.CD.R: 1 > test-metrics.R: 1 2 3 4 5 6 7 8 9 10 11 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: 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:1.2e-199 > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 13 of at most 60: > test-miss.CD.R: Convergence test P-value:6.6e-211 > 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: 1 2 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:2.2e-203 > 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 > 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: Convergence test P-value:5.2e-197 > test-miss.CD.R: 1 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-metrics.R: The log-likelihood improved by 0.0821. > test-miss.CD.R: Convergence test P-value:1e-201 > test-miss.CD.R: 1 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.CD.R: Convergence test P-value:2.1e-202 > test-miss.CD.R: 1 > test-miss.CD.R: 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 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:6.3e-210 > 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: Maximizing the pseudolikelihood. > test-miss.R: MPLE estimate = -2.118156 with log-likelihood -120.6883 OK. > test-miss.R: Finished MPLE. > test-miss.R: Evaluating log-likelihood at the estimate. > test-miss.CD.R: Convergence test P-value:1.3e-213 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > 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: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.R: Model reinitialized. > 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 free parameter vector: > 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 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: Estimated gradient of the log-likelihood: > 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 > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.374066 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:2.6e-202 > test-miss.CD.R: 1 > test-miss.CD.R: 2 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:5.5e-202 > 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.CD.R: Convergence test P-value:7.2e-205 > 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 23 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: Estimated gradient of the log-likelihood: > 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 > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.647302 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:3.5e-207 > 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.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:1.2e-197 > test-miss.CD.R: 1 2 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 = 128. > test-miss.R: New constrained interval = 128. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.95412 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:4e-193 > 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.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 = 64. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -2.070021 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:1.8e-212 > 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.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:8e-208 > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 28 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: Estimated gradient of the log-likelihood: > 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. > 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:1.4e-198 > 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: 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:2.3e-204 > 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: 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.R: Running theta=[-2.132118, 0.000000]. > test-miss.CD.R: Convergence test P-value:2.5e-205 > test-miss.CD.R: 1 2 > 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.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 31 of at most 60: > test-miss.R: Running theta=[-2.128267, 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:9.4e-205 > test-miss.CD.R: 1 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.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.CD.R: Iteration 32 of at most 60: > test-miss.R: Running theta=[-2.1196, 0.0000]. > test-miss.CD.R: Convergence test P-value:2.8e-202 > 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: 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:3.3e-209 > test-miss.CD.R: 1 2 > 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: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 34 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.R: Running theta=[-2.129597, 0.000000]. > test-miss.R: Running theta=[-2.13056, 0.00000]. > test-miss.CD.R: Convergence test P-value:2.8e-201 > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 35 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.R: Running theta=[-2.130928, 0.000000]. > test-miss.R: Running theta=[-2.129965, 0.000000]. > test-miss.CD.R: Convergence test P-value:5.8e-206 > test-miss.CD.R: 1 2 > test-miss.R: Running theta=[-2.129002, 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=[-2.128039, 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:1.3e-192 > test-miss.CD.R: 1 2 > 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: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 37 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.CD.R: Convergence test P-value:5.1e-199 > 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: 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.R: Running theta=[-2.125518, 0.000000]. > test-miss.CD.R: Convergence test P-value:1.7e-208 > test-miss.R: Running theta=[-2.126481, 0.000000]. > test-miss.CD.R: 1 > test-miss.CD.R: 2 > 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 39 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.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: Convergence test P-value:1.2e-197 > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 40 of at most 60: > 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.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:1.1e-205 > 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.R: Running theta=[-2.12396, 0.00000]. > test-miss.R: Running theta=[-2.122997, 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=[-2.122035, 0.000000]. > test-miss.R: Running theta=[-2.121072, 0.000000]. > 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.CD.R: Convergence test P-value:1.2e-204 > test-miss.CD.R: 1 2 > 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 42 of at most 60: > test-miss.CD.R: Convergence test P-value:8.4e-196 > test-miss.CD.R: 1 2 > 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:7.5e-206 > 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: 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.CD.R: Convergence test P-value:1.9e-197 > test-miss.CD.R: 1 > 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.CD.R: 2 > 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.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 45 of at most 60: > 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:2.8e-211 > 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: Correct estimate = -1.663142 with log-likelihood -79.82064 . > test-miss.CD.R: Convergence test P-value:6.5e-203 > 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: 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.CD.R: Convergence test P-value:4.7e-204 > 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 48 of at most 60: > test-miss.R: 'ergm:MH_SPDyad'. > test-miss.R: Initializing model... > test-miss.R: Model initialized. > test-miss.R: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.R: Model reinitialized. > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -0.6631421 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:5.7e-209 > 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:4e-201 > 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: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:9.5e-218 > 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: Back from constrained MCMC. > test-miss.R: New interval = 512. > test-miss.R: New constrained interval = 256. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -33.15638 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: 2 3 4 5 6 7 8 Optimizing with step length 0.5368. > test-miss.CD.R: Convergence test P-value:5.7e-193 > 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 free parameter vector: > 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 52 of at most 60: > test-miss.CD.R: Convergence test P-value:2.5e-206 > 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 53 of at most 60: > test-miss.CD.R: Convergence test P-value:6e-198 > test-miss.CD.R: 1 2 > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 256. > test-miss.R: New constrained interval = 128. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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 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:1.5e-204 > 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.CD.R: Convergence test P-value:2.9e-195 > 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: Back from constrained MCMC. > test-miss.R: New interval = 128. > test-miss.R: New constrained interval = 64. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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.CD.R: Convergence test P-value:6.8e-200 > 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 = 64. > test-miss.R: New constrained interval = 32. > test-miss.R: Estimated gradient of the log-likelihood: > 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.CD.R: Convergence test P-value:8e-207 > test-miss.CD.R: 1 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: 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.CD.R: Convergence test P-value:3.6e-215 > test-miss.R: Model and proposals initialized. > test-miss.R: Initializing constrained model and proposals... > 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: Constrained model and proposals initialized. > test-miss.R: Using 16 bridges: > test-miss.R: 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.CD.R: Convergence test P-value:1.6e-199 > 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: 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.CD.R: Convergence test P-value:6.1e-198 > 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: 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=[-1.665033, 0.000000]. > test-miss.R: Running theta=[-1.664276, 0.000000]. Saving _problems/test-miss.CD-76.R > 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: 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: 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: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.R: Model reinitialized. > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks. > 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 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-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-miss.R: 1 > test-miss.R: 2 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 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-miss.R: 1 > test-miss.R: 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: 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-mple-cov.R: Finished MPLE. > test-mple-cov.R: Evaluating log-likelihood at the estimate. > test-miss.R: 3 > test-miss.R: 4 > test-miss.R: 5 > test-miss.R: 6 > 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: 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 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: 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: 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: 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. 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 4 > test-networkLite.R: 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 > 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 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 > test-networkLite.R: 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 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 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 > 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 3 4 5 6 7 8 9 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 > 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-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: 0.0247. Not converged with 99% confidence; increasing sample size. > test-networkLite.R: Iteration 5 of at most 60: > 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-nodrop.R: 1 Optimizing with step length 1.0000. > test-nodrop.R: The log-likelihood improved by 0.0005. > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > 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: 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-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 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 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-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: 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-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 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-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-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-nonident-test.R: Finished MPLE. > test-networkLite.R: 1 > test-networkLite.R: 2 3 > test-networkLite.R: 4 > test-networkLite.R: 5 6 7 8 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. 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-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 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-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: 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 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-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: The log-likelihood improved by 0.0176. > 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: 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 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-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-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 0.0419. > 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: 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 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: 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 > 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 2 > test-networkLite.R: 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-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-nonunique-names.R: 1 > test-nonunique-names.R: 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-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 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-offsets.R: > 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: 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 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-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: 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: 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: > 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: 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-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 0.1926. > 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-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 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-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-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-offsets.R: Bridging between the dyad-independent submodel and the full model... > test-offsets.R: Setting up bridge sampling... > 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-offsets.R: Using 16 bridges: 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-offsets.R: 2 > 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-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-networkLite.R: 1 Optimizing with step length 1.0000. > test-offsets.R: 10 > 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: 11 > test-offsets.R: 12 13 14 15 > test-offsets.R: 16 > test-networkLite.R: 1 Optimizing with step length 1.0000. > 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: 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: 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 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 > 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 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-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-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-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-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-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-offsets.R: 14 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 15 > 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-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-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. > 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 > test-scoping.R: 3 > test-scoping.R: 4 > test-scoping.R: 5 > test-scoping.R: 6 7 8 9 > test-scoping.R: 10 > test-scoping.R: 11 > test-scoping.R: 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-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-scoping.R: Using 16 bridges: 1 > test-scoping.R: 2 3 > test-scoping.R: 4 > test-scoping.R: 5 6 > test-scoping.R: 7 > test-scoping.R: 8 9 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: 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 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 > test-shrink-into-CH.R: 10 11 > test-shrink-into-CH.R: 12 > test-shrink-into-CH.R: 13 14 15 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-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-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 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-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-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: 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-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-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 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: Stochastic approximation algorithm with theta_0 equal to: > 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-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-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 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 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: 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-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-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 > 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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 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 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 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 0.0022. > 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. 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 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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 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-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-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 0.7240. > 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 0.6386. > 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 Optimizing with step length 0.8376. > 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: > 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 11 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 = 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 > test-term-Offset.R: Optimizing with step length 1.0000. > test-term-Offset.R: The log-likelihood improved by 0.0005. > 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 0.0076. > 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-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-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: 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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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: 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-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: Starting 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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.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-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.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-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-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: 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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-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-bipartite.R: Starting 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: 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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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: 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-bipartite.R: Starting 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: Finished MPLE. > 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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-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-flexible.R: Starting 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-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-flexible.R: Starting 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-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-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-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-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.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-flexible.R: In term 'nodemix' 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-flexible.R: Starting 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-flexible.R: Starting 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-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-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-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-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-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-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: > 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 0.0013. > 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: > 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-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-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-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: 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-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-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-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-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):0.9332403 > 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 0.8501229 0.9808577 > test-valued-sim.R: [2,] 0.9672299 NA 0.9683310 > test-valued-sim.R: [3,] 0.8330923 1.0127929 NA > test-valued-sim.R: Simulated vars (target=4): > test-valued-sim.R: [,1] [,2] [,3] > test-valued-sim.R: [1,] NA 4.290191 3.699857 > test-valued-sim.R: [2,] 4.185627 NA 4.195162 > test-valued-sim.R: [3,] 3.983195 3.945351 NA > test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3): > test-valued-sim.R: [1] 0.3074687 0.2800508 0.3157743 > 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 1 | WARN 0 | SKIP 1 | PASS 4300 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • empty test (1): ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-miss.CD.R:76:3'): curved+missing ───────────────────────────── Expected `abs(coef(cdfit)[1] - truth)/sqrt(cdfit$covar[1])` < 2. Actual comparison: 2.95 >= 2.00 Difference: 0.95 >= 0 [ FAIL 1 | WARN 0 | SKIP 1 | PASS 4300 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 4.11.0
Check: tests
Result: ERROR Running ‘requireNamespaceTest.R’ [5s/31s] Running ‘testthat.R’ [10m/17m] 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(statnet.common) Attaching package: 'statnet.common' The following objects are masked from 'package:base': attr, order, replace > library(ergm) Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.11.0 (2025-12-22), 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. Attaching package: 'ergm' The following object is masked from 'package:statnet.common': snctrl > > 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-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 > 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-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-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-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-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. > 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-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-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-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-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-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 0.1892. > 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 0.0072. > 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-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-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-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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-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-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. > 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-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-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: 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: > 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. > test-bridge-target.stats.R: 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-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-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: 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-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/Rtmp0UoJvo/working_dir/RtmpbWOzei/file1f892cf1b903_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: > 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 7 8 9 10 11 12 13 14 15 16 . > 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 2 3 4 5 6 7 > test-checkpointing.R: 1 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/Rtmp0UoJvo/working_dir/RtmpbWOzei/file1f892cf1b903_002.RData'. > 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-bridge-target.stats.R: 10 > test-bridge-target.stats.R: 11 > test-checkpointing.R: Fitting the dyad-independent submodel... > test-bridge-target.stats.R: 12 > test-bridge-target.stats.R: 13 > 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: 14 > test-bridge-target.stats.R: 15 > test-checkpointing.R: Using 16 bridges: 1 > test-checkpointing.R: 2 > test-checkpointing.R: 3 > test-checkpointing.R: 4 > test-checkpointing.R: 5 > test-bridge-target.stats.R: 16 > 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-bridge-target.stats.R: . > test-bridge-target.stats.R: Bridging finished. > 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-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/Rtmp0UoJvo/working_dir/RtmpbWOzei/file1f892cf1b903_002.RData'. > test-checkpointing.R: Iteration 1 of at most 60: > 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-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-checkpointing.R: 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 > 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-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'. > 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-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'. > 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-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: 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-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 '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: 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. > 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-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-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > 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-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: > 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-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-dind.R: > 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: > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > 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-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-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-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: . > 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-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-constraints.R: Evaluating log-likelihood at the estimate. > 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-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-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 > 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 > test-constraints.R: 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-drop.R: 1 > test-constraints.R: The log-likelihood improved by 0.07919. > test-constraints.R: Iteration 2 of at most 60: > test-drop.R: Optimizing with step length 1.0000. > 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-drop.R: The log-likelihood improved by 0.0005. > 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-drop.R: Convergence test p-value: < 0.0001. > test-drop.R: Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-constraints.R: Setting up bridge sampling... > test-drop.R: Evaluating log-likelihood at the estimate. > test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-drop.R: Fitting the dyad-independent submodel... > 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-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-drop.R: Bridging between the dyad-independent submodel and the full model... > test-drop.R: Setting up bridge sampling... > 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: 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-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-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-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: 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-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-drop.R: 1 > test-drop.R: Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0084. > 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: > 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-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-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 free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -5.244530e-01 2.592560e-01 -3.147987e-01 -9.254589e-01 2.322348e-12 > test-drop.R: Starting unconstrained MCMC... > test-drop.R: Back from unconstrained MCMC. > test-drop.R: New interval = 512. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -3.399177 -1.395062 -4.641975 -3.370370 2.170829 > 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-drop.R: Estimating equations are not within tolerance region. > test-drop.R: > test-drop.R: Iteration 2 of at most 3 with free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -7.679134e-01 3.298974e-01 -4.787791e-01 -1.324882e+00 9.046976e-10 > test-drop.R: Starting unconstrained MCMC... > test-drop.R: Back from unconstrained MCMC. > test-drop.R: New interval = 256. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -0.2427984 0.4115226 -0.1934156 -0.5020576 -0.2889661 > test-drop.R: Starting MCMLE Optimization... > test-drop.R: 1 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 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 free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -1.0610706 1.2599949 -0.5094541 -1.4594441 0.1742152 > test-drop.R: Starting unconstrained MCMC... > test-drop.R: Back from unconstrained MCMC. > test-drop.R: New interval = 128. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -0.4855967 -0.3621399 -0.6090535 -0.5209768 0.5709312 > test-drop.R: Starting MCMLE Optimization... > test-drop.R: 1 Optimizing with step length 0.9524. > 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 0.0416. > test-drop.R: Distance from origin on tolerance region scale: 0.9245623 (previously Inf). > 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 = 10.41146, with 4 free parameter(s) and 238.9876 degrees of freedom. > test-drop.R: Convergence test p-value: 0.0387. 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. > test-drop.R: 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=[-2.0110138, -Inf, 1.4621780,-0.4923718, -Inf,-0.9527669, 0.1279654, 0.0000000]. > test-drop.R: Running theta=[-2.0515328, -Inf, 1.3678439,-0.4606058, -Inf,-0.8912980, 0.1197096, 0.0000000]. > test-drop.R: Running theta=[-2.0920517, -Inf, 1.2735099,-0.4288399, -Inf,-0.8298292, 0.1114537, 0.0000000]. > test-drop.R: Running theta=[-2.1325706, -Inf, 1.1791758,-0.3970740, -Inf,-0.7683604, 0.1031979, 0.0000000]. > test-drop.R: Running theta=[-2.17308956, -Inf, 1.08484175,-0.36530808, -Inf,-0.70689155, 0.09494207, 0.00000000]. > test-drop.R: Running theta=[-2.21360850, -Inf, 0.99050769,-0.33354216, -Inf,-0.64542272, 0.08668623, 0.00000000]. > test-drop.R: Running theta=[-2.2541274, -Inf, 0.8961736,-0.3017762, -Inf,-0.5839539, 0.0784304, 0.0000000]. > test-drop.R: Running theta=[-2.29464637, -Inf, 0.80183956,-0.27001032, -Inf,-0.52248506, 0.07017457, 0.00000000]. > test-drop.R: Running theta=[-2.33516531, -Inf, 0.70750549,-0.23824440, -Inf,-0.46101623, 0.06191874, 0.00000000]. > test-drop.R: Running theta=[-2.37568425, -Inf, 0.61317143,-0.20647848, -Inf,-0.39954740, 0.05366291, 0.00000000]. > test-drop.R: Running theta=[-2.41620318, -Inf, 0.51883736,-0.17471256, -Inf,-0.33807857, 0.04540708, 0.00000000]. > test-drop.R: Running theta=[-2.45672212, -Inf, 0.42450329,-0.14294664, -Inf,-0.27660974, 0.03715124, 0.00000000]. > test-drop.R: Running theta=[-2.49724105, -Inf, 0.33016923,-0.11118072, -Inf,-0.21514091, 0.02889541, 0.00000000]. > test-drop.R: Running theta=[-2.53775999, -Inf, 0.23583516,-0.07941480, -Inf,-0.15367208, 0.02063958, 0.00000000]. > test-drop.R: Running theta=[-2.57827893, -Inf, 0.14150110,-0.04764888, -Inf,-0.09220325, 0.01238375, 0.00000000]. > test-drop.R: Running theta=[-2.618797861, -Inf, 0.047167033,-0.015882960, -Inf,-0.030734415, 0.004127916, 0.000000000]. > 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.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-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-ergm.bridge.llr.R: 7 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: 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-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.bridge.llr.R: 2 > 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) > test-ergm-term-doc.R: 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: 3 > test-ergm-term-doc.R: Found 50 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: I(formula) (valued) > test-ergm-term-doc.R: Substitute a formula into the model formula > 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: cdf(min = NULL, max = NULL, by = NULL, margin = 0.1, nmax = 100) (valued) > test-ergm-term-doc.R: Empirical cumulative distribution function (unnormalized) of > test-ergm-term-doc.R: the network's dyad values > 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.bridge.llr.R: 4 > test-ergm-term-doc.R: Found 17 matching ergm terms: > test-ergm-term-doc.R: ChangeStats(fix, check_dind = TRUE) > test-ergm-term-doc.R: Specified statistics must remain constant > test-ergm-term-doc.R: > 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 > test-ergm-term-doc.R: 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 changestats > test-ergm-term-doc.R: > test-ergm.bridge.llr.R: 5 > test-ergm-term-doc.R: No proposals named 'mandomtoggle' were found. Try searching with search='mandomtoggle'instead. > test-ergm.bridge.llr.R: 6 > test-ergm.bridge.llr.R: 7 > test-ergm.bridge.llr.R: 8 > 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: 9 10 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-ergm.bridge.llr.R: Using 16 bridges: 1 > 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: 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-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-ergm.bridge.llr.R: 8 > 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-gflomiss.R: Maximizing the pseudolikelihood. > test-gflomiss.R: Finished MPLE. > test-gflomiss.R: Evaluating log-likelihood at the estimate. > 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: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gflomiss.R: Iteration 1 of at most 60: > 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-gflomiss.R: 1 > test-gflomiss.R: Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: Using 16 bridges: > test-ergm.bridge.llr.R: 1 > test-gflomiss.R: The log-likelihood improved by 0.0033. > 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: 2 > 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: 3 > test-gflomiss.R: Using 16 bridges: > test-gflomiss.R: 1 > test-ergm.bridge.llr.R: 4 > test-gflomiss.R: 2 > test-gflomiss.R: 3 > test-gflomiss.R: 4 > test-gflomiss.R: 5 > test-gflomiss.R: 6 > test-gflomiss.R: 7 > test-gflomiss.R: 8 > test-ergm.bridge.llr.R: 5 > test-gflomiss.R: 9 > test-gflomiss.R: 10 > test-ergm.bridge.llr.R: 6 > test-gflomiss.R: 11 > test-ergm.bridge.llr.R: 7 > test-gflomiss.R: 12 > test-ergm.bridge.llr.R: 8 > test-gflomiss.R: 13 > test-gflomiss.R: 14 > test-gflomiss.R: 15 > test-ergm.bridge.llr.R: 9 > test-gflomiss.R: 16 > test-ergm.bridge.llr.R: 10 > 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: 11 > test-ergm.bridge.llr.R: 12 > test-ergm.bridge.llr.R: 13 > test-gflomiss.R: 1 Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 14 > test-gflomiss.R: The log-likelihood improved by 0.0025. > test-ergm.bridge.llr.R: 15 > 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: 16 > 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-ergm.bridge.llr.R: . > test-gflomiss.R: 1 > test-gflomiss.R: 2 > test-gflomiss.R: 3 > test-gflomiss.R: 4 > test-gflomiss.R: 5 > test-gflomiss.R: 6 > test-gflomiss.R: 7 > test-ergm.bridge.llr.R: Fitting the dyad-independent submodel... > test-gflomiss.R: 8 > test-gflomiss.R: 9 > test-gflomiss.R: 10 > test-gflomiss.R: 11 > 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: 12 > test-gflomiss.R: 13 > test-gflomiss.R: 14 > test-ergm.bridge.llr.R: Using 16 bridges: > test-gflomiss.R: 15 > test-ergm.bridge.llr.R: 1 > test-gflomiss.R: 16 > test-ergm.bridge.llr.R: 2 > 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: 3 > 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: 4 > test-ergm.bridge.llr.R: 5 > test-ergm.bridge.llr.R: 6 > test-ergm.bridge.llr.R: 7 > 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: 8 > test-ergm.bridge.llr.R: 9 > 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: 10 > test-gmonkmiss.R: Iteration 1 of at most 3: > 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-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 0.6245. > 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-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: The log-likelihood improved by 0.0078. > 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-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-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-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-gof.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-gof.R: Starting contrastive divergence estimation via CD-MCMLE: > test-gof.R: Iteration 1 of at most 60: > test-gof.R: Convergence test P-value:5.5e-307 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 1.755. > test-gof.R: Iteration 2 of at most 60: > test-gof.R: Convergence test P-value:1.4e-100 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.5849. > test-gof.R: Iteration 3 of at most 60: > test-gof.R: Convergence test P-value:9e-24 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.1098. > test-gof.R: Iteration 4 of at most 60: > test-gof.R: Convergence test P-value:1.1e-05 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.02449. > test-gof.R: Iteration 5 of at most 60: > test-gof.R: Convergence test P-value:4.6e-03 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.01353. > test-gof.R: Iteration 6 of at most 60: > test-gof.R: Convergence test P-value:4.4e-02 > test-gof.R: 1 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: Optimizing with step length 1.0000. > test-gof.R: The log-likelihood improved by 0.009052. > test-gof.R: Iteration 7 of at most 60: > test-gmonkmiss.R: The log-likelihood improved by 0.4514. > test-gof.R: Convergence test P-value:5.2e-01 > test-gof.R: Convergence detected. Stopping. > test-gmonkmiss.R: Estimating equations are not within tolerance region. > test-gmonkmiss.R: Iteration 2 of at most 3: > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.003305. > test-gof.R: Finished CD. > test-gof.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.R: Iteration 1 of at most 2: > test-gmonkmiss.R: 1 > 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-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-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gmonkmiss.R: Iteration 1 of at most 3: > 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-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-gof.R: 1 > test-gof.R: Optimizing with step length 1.0000. > test-gof.R: The log-likelihood improved by 0.4463. > test-gof.R: Estimating equations are not within tolerance region. > test-gof.R: Iteration 2 of at most 2: > test-gof.R: 1 > test-gof.R: Optimizing with step length 1.0000. > test-gof.R: The log-likelihood improved by 0.0083. > test-gof.R: Convergence test p-value: 0.0005. > test-gof.R: Converged with 99% confidence. > test-gof.R: Finished MCMLE. > test-gof.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.R: for degeneracy, use the mcmc.diagnostics() function. > test-gof.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-gof.R: > test-gof.R: Goodness-of-fit for > test-gof.R: model statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: sum 168 143 171.39 204 0.88 > test-gof.R: nonzero 88 74 89.38 107 1.00 > test-gof.R: nodematch.sum.group.Loyal 49 32 50.21 69 0.90 > test-gof.R: nodematch.sum.group.Outcasts 20 9 20.67 28 0.90 > test-gof.R: nodematch.sum.group.Turks 59 36 58.71 78 1.00 > test-gof.R: > test-gof.R: Goodness-of-fit for cumulative distribution function > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: 0 218 199 216.62 232 1.00 > test-gof.R: 1 256 239 251.91 261 0.56 > test-gof.R: 2 276 270 278.08 289 0.88 > test-gof.R: 3 306 306 306.00 306 1.00 > test-gof.R: 4 306 306 306.00 306 1.00 > test-gof.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-gof.R: > test-gof.R: Goodness-of-fit for model statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: sum 168 125 167.79 204 1.00 > test-gof.R: nonzero 88 65 88.31 102 0.88 > test-gof.R: nodematch.sum.group.Loyal 49 29 50.04 65 0.94 > test-gof.R: nodematch.sum.group.Outcasts 20 10 20.15 33 1.00 > test-gof.R: nodematch.sum.group.Turks 59 39 58.45 87 1.00 > test-gof.R: > test-gof.R: Goodness-of-fit for user statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: atmost.0 218 204 217.69 241 0.88 > test-gof.R: atmost.1 256 241 252.91 268 0.70 > test-gof.R: atmost.2 276 264 279.61 290 0.44 > test-gof.R: atmost.3 306 306 306.00 306 1.00 > test-gof.R: atmost.4 306 306 306.00 306 1.00 > test-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 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-miss-dep.R: Convergence test P-value:4.6e-47 > test-miss-dep.R: 1 > 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-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-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-metrics.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-metrics.R: Optimizing with step length 0.4613. > test-miss-dep.R: Iteration 1 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-metrics.R: 1 > test-metrics.R: 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 > 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: 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-metrics.R: 1 Optimizing with step length 1.0000. > test-miss-dep.R: Setting up bridge sampling... > 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-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-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > 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-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 0.4018. > 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.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-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 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.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-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.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-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 0.0377. > test-miss.CD.R: Convergence test P-value:7.9e-01 > test-miss.CD.R: Convergence detected. Stopping. > 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: 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-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 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: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-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > 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-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: 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-metrics.R: 1 Optimizing with step length 1.0000. > 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-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-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: 2 > test-metrics.R: 3 > test-metrics.R: 4 5 6 > test-metrics.R: 7 > test-metrics.R: 8 9 > test-metrics.R: 10 > test-metrics.R: 11 > test-metrics.R: Optimizing with step length 0.3934. > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE: > test-miss.CD.R: Iteration 1 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:1.3e-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-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: 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-metrics.R: 1 Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 0.3360. > test-miss.CD.R: Convergence test P-value:1.8e-283 > 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 1.711. > test-miss.CD.R: Iteration 2 of at most 60: > test-miss.CD.R: Convergence test P-value:2.9e-233 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by 1.753. > 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:4.4e-193 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > 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: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 4 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:1.5e-199 > 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-miss.CD.R: Convergence test P-value:2e-201 > 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:1.3e-208 > 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: 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 > test-metrics.R: 10 > test-metrics.R: 11 Optimizing with step length 0.3701. > test-miss.CD.R: Convergence test P-value:4.7e-207 > test-miss.CD.R: 1 > test-miss.CD.R: 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 8 of at most 60: > test-miss.CD.R: Convergence test P-value:2.7e-202 > 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:4.1e-205 > 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 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-metrics.R: 7 8 > test-metrics.R: 9 10 > test-miss.CD.R: Iteration 10 of at most 60: > test-metrics.R: 11 12 > test-metrics.R: 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:1.4e-210 > 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-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: Convergence test P-value:5.2e-187 > 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:1.2e-199 > 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 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 13 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. > 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:6.6e-211 > 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 14 of at most 60: > test-miss.CD.R: Convergence test P-value:2.2e-203 > 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:5.2e-197 > 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 > 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 > 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:1e-201 > 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.CD.R: Convergence test P-value:2.1e-202 > 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-metrics.R: 1 > test-metrics.R: 2 3 > test-metrics.R: 4 > test-metrics.R: 5 6 > test-metrics.R: 7 8 > test-metrics.R: 9 10 > test-metrics.R: 11 12 > test-metrics.R: Optimizing with step length 0.6225. > test-miss.CD.R: Convergence test P-value:6.3e-210 > test-miss.CD.R: 1 2 > 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 19 of at most 60: > test-miss.CD.R: Convergence test P-value:1.3e-213 > 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 > 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: Convergence test P-value:2.6e-202 > 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 > test-metrics.R: 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:5.5e-202 > 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:7.2e-205 > 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 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:3.5e-207 > 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.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Finished MPLE. > test-miss.CD.R: Iteration 24 of at most 60: > 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.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:1.2e-197 > 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: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.R: Model reinitialized. > 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: -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.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 25 of at most 60: > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.118156 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:4e-193 > 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: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:1.8e-212 > 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 27 of at most 60: > test-miss.CD.R: Convergence test P-value:8e-208 > test-miss.CD.R: 1 2 > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 512. > test-miss.R: New constrained interval = 256. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -49.45267 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 2 > test-miss.R: 3 > test-miss.R: 4 5 > test-miss.R: 6 7 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 28 of at most 60: > test-miss.R: 8 9 10 > test-miss.R: 11 > test-miss.CD.R: Convergence test P-value:1.4e-198 > test-miss.CD.R: 1 2 > test-miss.R: 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 free parameter vector: > 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 29 of at most 60: > test-miss.CD.R: Convergence test P-value:2.3e-204 > 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 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: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -33.36008 > 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 0.4981. > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.647302 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:2.5e-205 > 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.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:9.4e-205 > 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:2.8e-202 > 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-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 128. > test-miss.R: New constrained interval = 128. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -17.50766 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.95412 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:3.3e-209 > 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 34 of at most 60: > test-miss.CD.R: Convergence test P-value:2.8e-201 > 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 = 64. > test-miss.R: New constrained interval = 64. > test-miss.R: Estimated gradient of the log-likelihood: > 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.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 free parameter vector: > 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.CD.R: Convergence test P-value:5.8e-206 > test-miss.CD.R: 1 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Iteration 36 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: Estimated gradient of the log-likelihood: > 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.CD.R: Convergence test P-value:1.3e-192 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > 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.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 37 of at most 60: > test-miss.R: Fitting the dyad-independent submodel... > test-miss.CD.R: Convergence test P-value:5.1e-199 > 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: 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:1.7e-208 > test-miss.R: Model and proposals initialized. > test-miss.R: Initializing constrained model and proposals... > 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: 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: Convergence test P-value:1.2e-197 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.131155, 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=[-2.130192, 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.CD.R: Convergence test P-value:1.1e-205 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.125378, 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=[-2.124415, 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.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:1.2e-204 > test-miss.CD.R: 1 > 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: 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 42 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.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:8.4e-196 > test-miss.CD.R: 1 > test-miss.CD.R: 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.R: Running theta=[-2.129002, 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=[-2.128039, 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.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:7.5e-206 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > 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: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 44 of at most 60: > 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.R: Running theta=[-2.123593, 0.000000]. > test-miss.R: Running theta=[-2.124555, 0.000000]. > test-miss.CD.R: Convergence test P-value:1.9e-197 > test-miss.CD.R: 1 > test-miss.CD.R: 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.CD.R: Convergence test P-value:2.8e-211 > test-miss.CD.R: 1 > test-miss.R: Running theta=[-2.131296, 0.000000]. > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.132259, 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=[-2.133222, 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.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.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.R: Running theta=[-2.121072, 0.000000]. > 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:6.5e-203 > 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:4.7e-204 > test-miss.CD.R: 1 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:5.7e-209 > 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.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 = > test-miss.R: > test-miss.R: 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: Convergence test P-value:4e-201 > 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 50 of at most 60: > test-miss.CD.R: Convergence test P-value:9.5e-218 > 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: Correct estimate = -1.663142 with log-likelihood -79.82064 . > test-miss.CD.R: Convergence test P-value:5.7e-193 > 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: 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.CD.R: Convergence test P-value:2.5e-206 > test-miss.CD.R: 1 2 > test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK. > test-miss.R: > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 53 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:6e-198 > 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: 'ergm:MH_SPDyad'. > test-miss.R: Initializing model... > test-miss.R: Model initialized. > test-miss.R: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.CD.R: Convergence test P-value:1.5e-204 > test-miss.CD.R: 1 2 > test-miss.R: Model reinitialized. > test-miss.R: Using initial method 'MPLE'. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > 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.CD.R: Iteration 55 of at most 60: > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -0.6631421 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:2.9e-195 > 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.CD.R: Convergence test P-value:6.8e-200 > 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 unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:8e-207 > 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:3.6e-215 > test-miss.R: Back from constrained MCMC. > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: New interval = 512. > test-miss.R: New constrained interval = 256. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 59 of at most 60: > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.146333 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:1.6e-199 > 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.R: Back from constrained MCMC. > test-miss.R: New interval = 256. > test-miss.R: New constrained interval = 128. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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 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:6.1e-198 > 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. Saving _problems/test-miss.CD-76.R > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 128. > test-miss.R: New constrained interval = 64. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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-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 > test-miss.R: 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: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.R: Model reinitialized. > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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-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 > 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 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 > 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: 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 > 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-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 4 5 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 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 > test-miss.R: 4 5 > test-miss.R: 6 > test-miss.R: 7 > test-miss.R: 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 > 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-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: 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-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-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: > 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. 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 > test-networkLite.R: 5 6 > test-networkLite.R: 7 > test-networkLite.R: 8 9 > test-networkLite.R: 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 > 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 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 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 3 > test-networkLite.R: 4 > test-networkLite.R: 5 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 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 2 3 > test-networkLite.R: 4 5 > test-networkLite.R: 6 > test-networkLite.R: 7 8 > test-networkLite.R: 9 10 11 > test-networkLite.R: 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. > 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: 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: 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: 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 > 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: 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: 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 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-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 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000. > 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: 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-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: 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-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 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-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-networkLite.R: 1 > test-networkLite.R: 2 > test-networkLite.R: 3 > 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-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-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: 0.0019. 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-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: 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: 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-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-nonident-test.R: Finished MPLE. > 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-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 0.1368. > 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-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 > 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-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-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: 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: 2 > test-networkLite.R: 3 > test-networkLite.R: 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 > test-networkLite.R: 15 > test-networkLite.R: 16 17 > test-networkLite.R: 18 > test-networkLite.R: 19 > test-networkLite.R: 20 > test-networkLite.R: 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 > test-networkLite.R: 4 > test-networkLite.R: 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: 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-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: 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-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-offsets.R: > test-networkLite.R: 1 > 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-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-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: 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: > 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 0.0419. > 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-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: 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 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 > 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-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: 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 0.0094. > 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: 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-offsets.R: 2 > test-offsets.R: 3 > test-offsets.R: 4 > test-offsets.R: 5 > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-offsets.R: 6 > test-offsets.R: 7 > 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-offsets.R: 8 > 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-offsets.R: 9 > 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: 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-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > 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: 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: 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 0.0078. > 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. 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 > 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. 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 > 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-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-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: > test-operators.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project > test-operators.R: * 'news(package="ergm.count")' for changes since last version > test-operators.R: * 'citation("ergm.count")' for citation information > test-operators.R: * 'https://statnet.org' for help, support, and other information > test-operators.R: > 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-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: 4 > test-offsets.R: 5 > test-offsets.R: 6 > 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: 7 > test-offsets.R: 8 > 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: 9 > 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: 10 > 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-offsets.R: 13 > test-predict.ergm.R: Maximizing the pseudolikelihood. > test-predict.ergm.R: Finished MPLE. > test-offsets.R: 14 > 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: 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-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-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-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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-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: > test-scoping.R: 1 > test-scoping.R: 2 > test-scoping.R: 3 > test-scoping.R: 4 > test-scoping.R: 5 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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-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-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 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 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-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-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 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 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-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-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-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: Stochastic approximation algorithm with theta_0 equal to: > 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: 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-stocapprox.R: 1 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. > 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 > 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 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-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-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-target-offset.R: Using 16 bridges: 1 2 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-target-offset.R: 10 > test-target-offset.R: 11 > test-target-offset.R: 12 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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 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-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 0.7240. > 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 0.6386. > 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 > test-target-offset.R: Optimizing with step length 0.8376. > 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: > 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 = 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-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. > 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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'. > 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-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-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-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-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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: 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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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-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-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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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) 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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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) 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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-flexible.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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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: In term 'absdiffcat' 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-flexible.R: Starting 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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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: 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-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 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-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-flexible.R: Starting 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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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: 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-flexible.R: Starting 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-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: In term 'nodematch' 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-flexible.R: Starting 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: In term 'nodemix' 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-flexible.R: Starting 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-flexible.R: Starting 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-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-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-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-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-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: 1 2 3 4 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: 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-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-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-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-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-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: 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-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: [,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: * 'c > test-valued-terms.R: itation("ergm.count")' for citation information > test-valued-terms.R: * 'https://statnet.org' for help, support, and other information > test-valued-terms.R: [ FAIL 1 | WARN 0 | SKIP 1 | PASS 4300 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • empty test (1): ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-miss.CD.R:76:3'): curved+missing ───────────────────────────── Expected `abs(coef(cdfit)[1] - truth)/sqrt(cdfit$covar[1])` < 2. Actual comparison: 2.95 >= 2.00 Difference: 0.95 >= 0 [ FAIL 1 | WARN 0 | SKIP 1 | PASS 4300 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 4.11.0
Check: tests
Result: ERROR Running ‘requireNamespaceTest.R’ [5s/14s] Running ‘testthat.R’ [10m/12m] 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(statnet.common) Attaching package: 'statnet.common' The following objects are masked from 'package:base': attr, order, replace > library(ergm) Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.11.0 (2025-12-22), 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. Attaching package: 'ergm' The following object is masked from 'package:statnet.common': snctrl > > 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 > 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 > 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 > 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: 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-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: > 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-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: 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-bridge-target.stats.R: 1 Optimizing with step length 1.0000. > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-bridge-target.stats.R: The log-likelihood improved by 0.0219. > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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-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: Bridging between the dyad-independent submodel and the full model... > test-bridge-target.stats.R: Setting up bridge sampling... > test-basis.R: Convergence test P-value:4.7e-173 > test-basis.R: 1 > test-bridge-target.stats.R: Using 16 bridges: 1 > test-bridge-target.stats.R: 2 > test-basis.R: The log-likelihood improved by 1.861. > test-basis.R: Iteration 2 of at most 60: > test-bridge-target.stats.R: 3 > 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-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-bridge-target.stats.R: 5 > 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: 6 > test-bridge-target.stats.R: 7 > 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-bridge-target.stats.R: 8 > test-basis.R: 1 > test-bridge-target.stats.R: 9 > 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: 10 > test-bridge-target.stats.R: 11 > 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: 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-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 0.1892. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-bridge-target.stats.R: 4 > 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: 5 > 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-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: Using 16 bridges: 1 > test-basis.R: 2 > test-bridge-target.stats.R: 9 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-bridge-target.stats.R: 10 > 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-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: . > 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: 14 > test-bridge-target.stats.R: 15 > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-bridge-target.stats.R: 16 > 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-bridge-target.stats.R: . > test-bridge-target.stats.R: Bridging finished. > 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: Fitting the dyad-independent submodel... > 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: Bridging between the dyad-independent submodel and the full model... > test-bridge-target.stats.R: Setting up bridge sampling... > 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: 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-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-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: 8 > 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: 9 > 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-bridge-target.stats.R: 10 > 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: 11 > 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-bridge-target.stats.R: 12 > 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: 13 > 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: 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: > test-bridge-target.stats.R: 1 > 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: 2 > test-bridge-target.stats.R: 3 > test-bridge-target.stats.R: 4 > 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: 5 > 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. 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-basis.R: 6 > test-bridge-target.stats.R: 7 > 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-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-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/RtmpKHbuQM/working_dir/RtmpAQ4tv2/file939ae2af911b1_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-bridge-target.stats.R: 5 > 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/RtmpKHbuQM/working_dir/RtmpAQ4tv2/file939ae2af911b1_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-checkpointing.R: 1 > 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-bridge-target.stats.R: 12 > test-checkpointing.R: Fitting the dyad-independent submodel... > test-bridge-target.stats.R: 13 > 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: 14 > 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-checkpointing.R: 8 > test-checkpointing.R: 9 > test-checkpointing.R: 10 > test-bridge-target.stats.R: 15 > 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-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: Resuming from state saved in '/tmp/RtmpKHbuQM/working_dir/RtmpAQ4tv2/file939ae2af911b1_002.RData'. > test-checkpointing.R: Iteration 1 of at most 60: > 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-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-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-checkpointing.R: 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 > 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-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: * 'c > test-constrain-blockdiag.R: itation("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-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 '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. > test-constrain-dind.R: Fitting the dyad-independent submodel... > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > 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-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: 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-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-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: . > 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: 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-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: > 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 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-constraints.R: > test-drop.R: 1 > test-drop.R: Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0011. > 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-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: 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-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-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 0.0005. > 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: > test-drop.R: 1 > test-drop.R: 2 > 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: 3 > test-constraints.R: > 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-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 free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -5.244530e-01 2.592560e-01 -3.147987e-01 -9.254589e-01 2.322371e-12 > 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: 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: Back from unconstrained MCMC. > test-drop.R: New interval = 512. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -3.399177 -1.395062 -4.641975 -3.370370 2.170829 > 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-drop.R: Estimating equations are not within tolerance region. > test-drop.R: > test-drop.R: Iteration 2 of at most 3 with free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -7.679134e-01 3.298974e-01 -4.787791e-01 -1.324882e+00 9.046975e-10 > test-drop.R: Starting unconstrained MCMC... > 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-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: Back from unconstrained MCMC. > test-drop.R: New interval = 256. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -0.2427984 0.4115226 -0.1934156 -0.5020576 -0.2889661 > test-drop.R: Starting MCMLE Optimization... > test-drop.R: 1 > test-constraints.R: 10 > 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 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 free parameter vector: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -1.0610706 1.2599949 -0.5094541 -1.4594441 0.1742152 > test-drop.R: Starting unconstrained MCMC... > 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 > 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 > test-constraints.R: 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 > 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: Back from unconstrained MCMC. > test-drop.R: New interval = 128. > test-drop.R: Estimated gradient of the log-likelihood: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -0.4855967 -0.3621399 -0.6090535 -0.5209768 0.5709312 > test-drop.R: Starting MCMLE Optimization... > test-drop.R: 1 Optimizing with step length 0.9524. > 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 0.0419. > test-drop.R: Distance from origin on tolerance region scale: 0.9245623 (previously Inf). > 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 = 10.22576, with 4 free parameter(s) and 238.9876 degrees of freedom. > test-drop.R: Convergence test p-value: 0.0416. 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. > test-drop.R: Initializing model to obtain the list of dyad-independent terms... > test-drop.R: Fitting the dyad-independent submodel... > 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-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: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-drop.R: Initializing model and proposals... > test-constraints.R: Using 16 bridges: > test-constraints.R: 1 > test-constraints.R: 2 > test-drop.R: Model and proposals initialized. > test-drop.R: Using 16 bridges: > test-drop.R: Running theta=[-1.0957763, -Inf, 1.5214507,-0.6414651, -Inf,-1.4155954, 0.3778579, 0.0000000]. > test-drop.R: Running theta=[-1.1953428, -Inf, 1.4232926,-0.6000803, -Inf,-1.3242667, 0.3534799, 0.0000000]. > test-constraints.R: 3 > test-drop.R: Running theta=[-1.2949093, -Inf, 1.3251345,-0.5586954, -Inf,-1.2329380, 0.3291020, 0.0000000]. > test-drop.R: Running theta=[-1.3944759, -Inf, 1.2269764,-0.5173106, -Inf,-1.1416092, 0.3047241, 0.0000000]. > test-drop.R: Running theta=[-1.4940424, -Inf, 1.1288183,-0.4759257, -Inf,-1.0502805, 0.2803462, 0.0000000]. > test-constraints.R: 4 > test-drop.R: Running theta=[-1.5936089, -Inf, 1.0306601,-0.4345409, -Inf,-0.9589517, 0.2559682, 0.0000000]. > test-drop.R: Running theta=[-1.6931754, -Inf, 0.9325020,-0.3931560, -Inf,-0.8676230, 0.2315903, 0.0000000]. > test-drop.R: Running theta=[-1.7927419, -Inf, 0.8343439,-0.3517712, -Inf,-0.7762943, 0.2072124, 0.0000000]. > test-constraints.R: 5 > test-drop.R: Running theta=[-1.8923085, -Inf, 0.7361858,-0.3103863, -Inf,-0.6849655, 0.1828345, 0.0000000]. > test-drop.R: Running theta=[-1.9918750, -Inf, 0.6380277,-0.2690015, -Inf,-0.5936368, 0.1584565, 0.0000000]. > test-drop.R: Running theta=[-2.0914415, -Inf, 0.5398696,-0.2276166, -Inf,-0.5023081, 0.1340786, 0.0000000]. > test-constraints.R: 6 > test-drop.R: Running theta=[-2.1910080, -Inf, 0.4417115,-0.1862318, -Inf,-0.4109793, 0.1097007, 0.0000000]. > test-drop.R: Running theta=[-2.29057452, -Inf, 0.34355338,-0.14484696, -Inf,-0.31965058, 0.08532274, 0.00000000]. > test-drop.R: Running theta=[-2.39014104, -Inf, 0.24539527,-0.10346211, -Inf,-0.22832184, 0.06094482, 0.00000000]. > test-constraints.R: 7 > test-drop.R: Running theta=[-2.48970755, -Inf, 0.14723716,-0.06207727, -Inf,-0.13699311, 0.03656689, 0.00000000]. > test-drop.R: Running theta=[-2.58927407, -Inf, 0.04907905,-0.02069242, -Inf,-0.04566437, 0.01218896, 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-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-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-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: * '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-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 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 > test-ergm-term-doc.R: 50 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: I(formula) (valued) > test-ergm-term-doc.R: Substitute a formula into the model formula > 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: cdf(min = NULL, max = NULL, by = NULL, margin = 0.1, nmax = 100) (valued) > test-ergm-term-doc.R: Empirical cumulative distribution function (unnormalized) of > test-ergm-term-doc.R: the network's dyad values > 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 > test-ergm-term-doc.R: 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 > test-ergm-term-doc.R: 17 matching ergm terms: > test-ergm-term-doc.R: ChangeStats(fix, check_dind = TRUE) > test-ergm-term-doc.R: Specified statistics must remain constant > test-ergm-term-doc.R: > 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 changestats > 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-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: 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-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: 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-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: 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-gflomiss.R: 1 > test-gflomiss.R: Optimizing with step length 1.0000. > test-gflomiss.R: The log-likelihood improved by 0.0039. > test-ergm.bridge.llr.R: 8 > 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: 9 > 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-ergm.bridge.llr.R: 10 > test-gflomiss.R: 2 > test-gflomiss.R: 3 > test-gflomiss.R: 4 > test-gflomiss.R: 5 > test-ergm.bridge.llr.R: 11 > test-gflomiss.R: 6 > test-gflomiss.R: 7 > test-gflomiss.R: 8 > test-ergm.bridge.llr.R: 12 > test-gflomiss.R: 9 > test-gflomiss.R: 10 > test-gflomiss.R: 11 > test-ergm.bridge.llr.R: 13 > test-gflomiss.R: 12 > test-gflomiss.R: 13 > test-gflomiss.R: 14 > test-ergm.bridge.llr.R: 14 > test-gflomiss.R: 15 > test-gflomiss.R: 16 > test-ergm.bridge.llr.R: 15 > 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: 16 > 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: Bridging finished. > test-ergm.bridge.llr.R: Setting up bridge sampling... > test-ergm.bridge.llr.R: Using 16 bridges: > test-ergm.bridge.llr.R: 1 > test-gflomiss.R: 1 Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 2 > test-gflomiss.R: The log-likelihood improved by 0.0078. > 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: 3 > 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-ergm.bridge.llr.R: 4 > test-gflomiss.R: 3 > test-gflomiss.R: 4 > test-gflomiss.R: 5 > test-gflomiss.R: 6 > test-ergm.bridge.llr.R: 5 > 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-ergm.bridge.llr.R: 6 > 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: 7 > 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: 8 > 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: 9 > 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: 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-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: 16 > test-ergm.bridge.llr.R: . > test-ergm.bridge.llr.R: Setting up bridge sampling... > 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: Using 16 bridges: > test-ergm.bridge.llr.R: 1 > 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: 2 > 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-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-ergm.bridge.llr.R: 3 > 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-ergm.bridge.llr.R: 4 > 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 0.4514. > test-gmonkmiss.R: Estimating equations are not within tolerance region. > test-gmonkmiss.R: Iteration 2 of at most 3: > test-ergm.bridge.llr.R: 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-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-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-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-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-ergm.bridge.llr.R: 16 > test-gmonkmiss.R: Iteration 1 of at most 3: > 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-gmonkmiss.R: 1 > test-gmonkmiss.R: Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 5 > 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-ergm.bridge.llr.R: 9 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 10 > 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: 11 > test-ergm.bridge.llr.R: 12 > 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-gof.R: > 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-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-gof.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-gof.R: Starting contrastive divergence estimation via CD-MCMLE: > test-gof.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-gof.R: Convergence test P-value:1.8e-266 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 1.541. > test-gof.R: Iteration 2 of at most 60: > test-gof.R: Convergence test P-value:2e-184 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 1.461. > test-gof.R: Iteration 3 of at most 60: > test-gof.R: Convergence test P-value:1.8e-38 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.1713. > test-gof.R: Iteration 4 of at most 60: > test-gof.R: Convergence test P-value:5.5e-05 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.02153. > test-gof.R: Iteration 5 of at most 60: > test-gof.R: Convergence test P-value:3.3e-01 > 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-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.00457. > test-gof.R: Iteration 6 of at most 60: > test-gof.R: Convergence test P-value:4.4e-02 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.009062. > test-gof.R: Iteration 7 of at most 60: > test-gof.R: Convergence test P-value:4.6e-01 > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.003667. > test-gof.R: Iteration 8 of at most 60: > test-gof.R: Convergence test P-value:8.7e-01 > test-gof.R: Convergence detected. Stopping. > test-gof.R: 1 > test-gof.R: The log-likelihood improved by 0.001439. > test-gof.R: Finished CD. > test-gof.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.R: Iteration 1 of at most 2: > test-metrics.R: 1 > test-metrics.R: 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 > 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-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-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-gof.R: 1 > test-gof.R: Optimizing with step length 1.0000. > test-gof.R: The log-likelihood improved by 0.2102. > test-gof.R: Estimating equations are not within tolerance region. > test-gof.R: Iteration 2 of at most 2: > 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-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-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > test-gof.R: 1 > test-gof.R: Optimizing with step length 1.0000. > test-gof.R: The log-likelihood improved by 0.0698. > test-gof.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-gof.R: Finished MCMLE. > test-gof.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.R: for degeneracy, use the mcmc.diagnostics() function. > test-gof.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-metrics.R: 1 > test-gof.R: > test-gof.R: Goodness-of-fit for model statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: sum 168 124 167.55 196 0.98 > test-gof.R: nonzero 88 68 88.02 106 1.00 > test-gof.R: nodematch.sum.group.Loyal 49 33 51.56 74 0.78 > test-gof.R: nodematch.sum.group.Outcasts 20 9 19.55 32 0.98 > test-gof.R: nodematch.sum.group.Turks 59 39 58.04 74 0.96 > test-gof.R: > test-gof.R: Goodness-of-fit for cumulative distribution function > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: 0 218 200 217.98 238 1.00 > test-gof.R: 1 256 241 253.05 272 0.56 > test-gof.R: 2 276 271 279.42 290 0.48 > test-gof.R: 3 306 306 306.00 306 1.00 > test-gof.R: 4 306 306 306.00 306 1.00 > 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-gof.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 0.4804. > test-metrics.R: The log-likelihood improved by 2.5519. > test-gof.R: > test-gof.R: Goodness-of-fit for model statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: sum 168 131 163.65 201 0.76 > test-gof.R: nonzero 88 70 86.60 106 0.82 > test-gof.R: nodematch.sum.group.Loyal 49 25 48.56 68 0.94 > test-gof.R: nodematch.sum.group.Outcasts 20 12 19.57 31 0.88 > test-gof.R: nodematch.sum.group.Turks 59 31 57.33 77 0.88 > test-gof.R: > test-gof.R: Goodness-of-fit for user statistics > test-gof.R: > test-gof.R: obs min mean max MC p-value > test-gof.R: atmost.0 218 200 219.40 236 0.82 > test-gof.R: atmost.1 256 239 254.73 265 0.90 > test-gof.R: atmost.2 276 272 280.22 288 0.34 > test-gof.R: atmost.3 306 306 306.00 306 1.00 > test-gof.R: atmost.4 306 306 306.00 306 1.00 > 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-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 > test-miss-dep.R: 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:9.4e-47 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 1.617. > test-miss-dep.R: Iteration 2 of at most 60: > test-metrics.R: 1 Optimizing with step length 1.0000. > test-miss-dep.R: Convergence test P-value:2.6e-20 > test-miss-dep.R: 1 > test-metrics.R: The log-likelihood improved by 0.1226. > test-miss-dep.R: The log-likelihood improved by 0.4878. > test-miss-dep.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-dep.R: Convergence test P-value:2.6e-03 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.03706. > test-miss-dep.R: Iteration 4 of at most 60: > test-miss-dep.R: Convergence test P-value:1.1e-01 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.01002. > test-miss-dep.R: Iteration 5 of at most 60: > test-miss-dep.R: Convergence test P-value:9.4e-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: 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 0.0639. > 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-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 2 > test-metrics.R: 3 4 > test-metrics.R: 5 6 > test-metrics.R: 7 8 > test-metrics.R: 9 > test-metrics.R: 10 11 > test-metrics.R: Optimizing with step length 0.3934. > test-miss-dep.R: 5 > 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-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-metrics.R: 1 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-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-miss.CD.R: n=20, density=0.1, missing=0.1 > 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: 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 1.0000. > 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 0.0827. > 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: 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: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-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-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 > 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-metrics.R: 1 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: 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 > 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-metrics.R: 1 > test-metrics.R: 2 > test-metrics.R: 3 4 > test-metrics.R: 5 > test-metrics.R: 6 7 > 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: 8 > test-metrics.R: 9 > test-metrics.R: 10 11 > test-metrics.R: 12 > test-metrics.R: 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-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: 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 > 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: 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: 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 1.711. > test-miss.CD.R: Iteration 2 of at most 60: > test-miss.CD.R: Convergence test P-value:2.9e-233 > 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 0.0012. > test-miss.CD.R: The log-likelihood improved by 1.753. > test-miss.CD.R: Iteration 3 of at most 60: > 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: Convergence test P-value:4.4e-193 > 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 4 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:1.5e-199 > 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:2e-201 > 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:1.3e-208 > 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:4.7e-207 > 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:2.7e-202 > 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 2 > test-metrics.R: 3 > test-metrics.R: 4 > test-metrics.R: 5 > test-metrics.R: 6 > test-metrics.R: 7 > test-miss.CD.R: Convergence test P-value:4.1e-205 > test-metrics.R: 8 9 > test-metrics.R: 10 > test-metrics.R: 11 > test-miss.CD.R: 1 2 > 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: 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:1.4e-210 > 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:5.2e-187 > 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-metrics.R: 1 > test-metrics.R: 2 > test-metrics.R: 3 > test-metrics.R: 4 > test-metrics.R: 5 > test-miss.CD.R: Convergence test P-value:1.2e-199 > test-metrics.R: 6 > test-metrics.R: 7 8 9 > test-metrics.R: 10 11 > test-miss.CD.R: 1 2 > test-metrics.R: 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: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 13 of at most 60: > test-miss.CD.R: Convergence test P-value:6.6e-211 > 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:2.2e-203 > 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 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 15 of at most 60: > test-miss.CD.R: Convergence test P-value:5.2e-197 > 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:1e-201 > 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.CD.R: Convergence test P-value:2.1e-202 > 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 18 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:6.3e-210 > 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: n= > test-miss.R: 20, density=0.1, missing=0.05 > test-miss.CD.R: Convergence test P-value:1.3e-213 > 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: Correct estimate = -2.118156 with log-likelihood -120.6883 . > test-miss.CD.R: Convergence test P-value:2.6e-202 > 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: 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.CD.R: Convergence test P-value:5.5e-202 > test-miss.CD.R: 1 > test-miss.CD.R: 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 22 of at most 60: > test-miss.R: 'ergm:MH_SPDyad'. > test-miss.R: Initializing model... > test-miss.R: Model initialized. > test-miss.R: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.R: Model reinitialized. > 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.118156 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:7.2e-205 > 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 23 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-207 > 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-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 512. > test-miss.R: New constrained interval = 256. > test-miss.R: Estimated gradient of the log-likelihood: > 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 > test-miss.R: 5 6 > test-miss.R: 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.374066 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:1.2e-197 > 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.CD.R: Convergence test P-value:4e-193 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Back from unconstrained 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: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:1.8e-212 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 256. > test-miss.R: New constrained interval = 128. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -33.36008 > 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 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 free parameter vector: > 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:8e-208 > 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:1.4e-198 > 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-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 128. > test-miss.R: New constrained interval = 128. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -17.50766 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: 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 free parameter vector: > test-miss.R: edges > test-miss.R: -1.95412 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:2.3e-204 > 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.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:2.5e-205 > 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 constrained MCMC. > test-miss.CD.R: Convergence test P-value:9.4e-205 > test-miss.CD.R: 1 > test-miss.R: New interval = 64. > test-miss.R: New constrained interval = 64. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -5.621399 > test-miss.CD.R: 2 > 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 0.3258. > 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 free parameter vector: > 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 32 of at most 60: > test-miss.CD.R: Convergence test P-value:2.8e-202 > 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 33 of at most 60: > test-miss.CD.R: Convergence test P-value:3.3e-209 > 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 34 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-201 > 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 35 of at most 60: > test-miss.CD.R: Convergence test P-value:5.8e-206 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 32. > test-miss.R: New constrained interval = 32. > test-miss.R: Estimated gradient of the log-likelihood: > 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.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Starting MCMC s.e. computation. > test-miss.CD.R: Iteration 36 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. Converged with 99% confidence. > test-miss.R: Finished MCMLE. > test-miss.R: Evaluating log-likelihood at the estimate. > test-miss.CD.R: Convergence test P-value:1.3e-192 > test-miss.R: Initializing model to obtain the list of dyad-independent terms... > test-miss.CD.R: 1 2 > test-miss.R: Fitting the dyad-independent submodel... > 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:5.1e-199 > test-miss.CD.R: 1 > test-miss.CD.R: 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.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=[-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.R: Running theta=[-2.127304, 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=[-2.126341, 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.CD.R: Convergence test P-value:1.7e-208 > test-miss.CD.R: 1 > test-miss.R: Running theta=[-2.121526, 0.000000]. > 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 39 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:1.2e-197 > test-miss.R: Running theta=[-2.121894, 0.000000]. > test-miss.CD.R: 1 > test-miss.CD.R: 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.CD.R: Convergence test P-value:1.1e-205 > 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 41 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:1.2e-204 > test-miss.R: Running theta=[-2.132486, 0.000000]. > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Running theta=[-2.133449, 0.000000]. > test-miss.CD.R: Iteration 42 of at most 60: > 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.R: Running theta=[-2.130928, 0.000000]. > test-miss.CD.R: Convergence test P-value:8.4e-196 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > 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 43 of at most 60: > 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:7.5e-206 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.127076, 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=[-2.126113, 0.000000]. > test-miss.R: Running theta=[-2.125151, 0.000000]. > test-miss.CD.R: Convergence test P-value:1.9e-197 > test-miss.R: Running theta=[-2.124188, 0.000000]. > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.123225, 0.000000]. > 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 45 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.R: Running theta=[-2.119741, 0.000000]. > test-miss.CD.R: Convergence test P-value:2.8e-211 > test-miss.CD.R: 1 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 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.R: Running theta=[-2.126481, 0.000000]. > test-miss.CD.R: Convergence test P-value:6.5e-203 > 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.R: Running theta=[-2.12937, 0.00000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Running theta=[-2.130333, 0.000000]. > test-miss.CD.R: Iteration 47 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.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:4.7e-204 > test-miss.CD.R: 1 > test-miss.CD.R: 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.R: Running theta=[-2.12396, 0.00000]. > test-miss.R: Running theta=[-2.122997, 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=[-2.122035, 0.000000]. > test-miss.R: Running theta=[-2.121072, 0.000000]. > 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:5.7e-209 > 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.CD.R: Convergence test P-value:4e-201 > 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.CD.R: Convergence test P-value:9.5e-218 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Sample statistics summary: > test-miss.CD.R: The log-likelihood improved by < 0.0001. > 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.CD.R: Iteration 51 of at most 60: > 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 > 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: -0.4658741 > 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: Convergence test P-value:5.7e-193 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Correct estimate = > test-miss.R: -1.663142 with log-likelihood -79.82064 . > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 52 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.CD.R: Convergence test P-value:2.5e-206 > test-miss.R: Finished MPLE. > test-miss.CD.R: 1 2 > test-miss.R: Evaluating log-likelihood at the estimate. > test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 53 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: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.CD.R: Convergence test P-value:6e-198 > test-miss.R: Model reinitialized. > test-miss.CD.R: 1 > test-miss.R: Using initial method 'MPLE'. > test-miss.CD.R: 2 > 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 free parameter vector: > 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 54 of at most 60: > test-miss.CD.R: Convergence test P-value:1.5e-204 > 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.CD.R: Convergence test P-value:2.9e-195 > 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 56 of at most 60: > test-miss.CD.R: Convergence test P-value:6.8e-200 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 512. > test-miss.R: New constrained interval = 256. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -33.15638 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 2 > test-miss.R: 3 4 5 > test-miss.R: 6 7 > test-miss.R: 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 free parameter vector: > 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 57 of at most 60: > test-miss.CD.R: Convergence test P-value:8e-207 > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > 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 constrained MCMC. > test-miss.R: New interval = 256. > test-miss.R: New constrained interval = 128. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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.CD.R: Convergence test P-value:3.6e-215 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 128. > test-miss.R: New constrained interval = 64. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: -2.600823 > test-miss.R: Starting MCMLE Optimization... > test-miss.CD.R: Iteration 59 of at most 60: > 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 free parameter vector: > 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.CD.R: Convergence test P-value:1.6e-199 > test-miss.R: Back from constrained MCMC. > test-miss.CD.R: 1 > test-miss.R: New interval = 64. > test-miss.R: New constrained interval = 32. > test-miss.R: Estimated gradient of the log-likelihood: > test-miss.R: edges > test-miss.R: 0.2386831 > test-miss.R: Starting MCMLE Optimization... > test-miss.CD.R: 2 > 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. > 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.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:6.1e-198 > 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. Saving _problems/test-miss.CD-76.R > 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-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: 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 > 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 = > test-miss.R: -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: Constrained proposal requires different auxiliaries: reinitializing model... > test-miss.R: Model reinitialized. > 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 free parameter vector: > 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-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks. > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 512. > test-miss.R: New constrained interval = 256. > test-miss.R: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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 free parameter vector: > 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: Estimated gradient of the log-likelihood: > 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 > 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: > 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 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 5 > test-miss.R: 6 7 > test-miss.R: 8 > test-miss.R: 9 > test-miss.R: 10 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 8 > test-miss.R: 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-miss.R: 1 > test-miss.R: 2 3 > test-miss.R: 4 > test-miss.R: 5 > test-miss.R: 6 > test-miss.R: 7 > test-miss.R: 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-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 > 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 4 > test-miss.R: 5 > test-miss.R: 6 > test-miss.R: 7 > test-miss.R: 8 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-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks. > 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-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-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-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 > 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-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 > 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. > 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 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 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 > test-networkLite.R: 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 0.1793. > 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 > 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. > 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 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 0.0392. > 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 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 > test-networkLite.R: 5 > test-networkLite.R: 6 7 > test-networkLite.R: 8 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-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 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 0.0176. > 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-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 0.0103. > test-nodrop.R: 1 > test-nodrop.R: Optimizing with step length 1.0000. > test-nodrop.R: The log-likelihood improved by 0.0005. > 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: 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-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: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0419. > 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: 0.0085. 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-nodrop.R: 1 > test-nodrop.R: Optimizing with step length 0.2247. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > 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: Iteration 1 of at most 60: > test-networkLite.R: 1 2 > test-networkLite.R: 3 4 > test-networkLite.R: 5 > test-networkLite.R: 6 7 > test-networkLite.R: 8 > test-networkLite.R: 9 10 > test-networkLite.R: 11 12 > test-networkLite.R: 13 > test-networkLite.R: 14 > test-networkLite.R: 15 16 > test-networkLite.R: 17 18 > test-networkLite.R: 19 > test-networkLite.R: 20 > test-nodrop.R: 1 > test-networkLite.R: 21 22 > test-nodrop.R: Optimizing with step length 0.2817. > test-networkLite.R: 23 > test-networkLite.R: Optimizing with step length 1.0000. > 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-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-networkLite.R: 4 > test-networkLite.R: 5 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 10 > test-networkLite.R: 11 Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0094. > 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-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: 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 0.3098. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-nodrop.R: The log-likelihood improved by 2.4783. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > 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: 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-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 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: 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-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 > 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: 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: 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-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: 0.0247. Not converged with 99% confidence; increasing sample size. > test-networkLite.R: Iteration 5 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 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 0.0103. > 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 > 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 > 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-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-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: 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 12 > test-networkLite.R: 13 > test-networkLite.R: 14 > test-networkLite.R: 15 > test-networkLite.R: 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 > 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: 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-nonunique-names.R: 1 > test-nonunique-names.R: Optimizing with step length 1.0000. > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > 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'. > 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-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: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'. > 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 > 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-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'. > 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: 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-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 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-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 0.0160. > test-offsets.R: Finished MPLE. > test-offsets.R: Evaluating log-likelihood at the estimate. > test-offsets.R: > 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-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-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-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-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-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: 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-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-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and '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: > test-offsets.R: 1 > test-offsets.R: 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-offsets.R: 3 > 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-offsets.R: 4 > 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-offsets.R: 5 > 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: 6 > test-offsets.R: 7 > test-offsets.R: 8 > test-offsets.R: 9 > test-offsets.R: 10 > test-offsets.R: 11 > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-offsets.R: 12 > 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: 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-networkLite.R: 1 > 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 0.0006. > 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: 0.0458. > 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-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: 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 > 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-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: > test-offsets.R: 1 > test-offsets.R: 2 > 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 6 > test-offsets.R: 7 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 8 > 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 12 > test-offsets.R: 13 > 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-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. 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 > 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-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-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-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 > test-scoping.R: 7 > test-scoping.R: 8 > test-scoping.R: 9 > test-scoping.R: 10 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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 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 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 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-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: > 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. > test-skip.R: 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-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-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-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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-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-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-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-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: 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: 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: 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-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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-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 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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 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 > 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: > 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: > 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 0.7240. > 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 0.6386. > 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-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 0.8376. > 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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 > 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 = 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-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 > 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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'. > 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 latte > test-term-b12nodematch.R: r. 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-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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: 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > 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. > test-term-bipartite.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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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) 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-directed.R: Starting 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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-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: 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-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-bipartite.R: Starting 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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-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: 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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.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-directed.R: Obtaining the responsible dyads. > test-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-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-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: 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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting 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-flexible.R: Starting 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: 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-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 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-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-flexible.R: Starting 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: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > 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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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: 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: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-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-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-flexible.R: Starting 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-flexible.R: Starting 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-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-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-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: > 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: 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: 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. > 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: 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-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-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-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-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-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-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-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-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-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: > 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 1 | WARN 0 | SKIP 1 | PASS 4300 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • empty test (1): ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-miss.CD.R:76:3'): curved+missing ───────────────────────────── Expected `abs(coef(cdfit)[1] - truth)/sqrt(cdfit$covar[1])` < 2. Actual comparison: 2.95 >= 2.00 Difference: 0.95 >= 0 [ FAIL 1 | WARN 0 | SKIP 1 | PASS 4300 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 4.11.0
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 14.3.0-12) 14.3.0’ * used C++ compiler: ‘g++-14 (Debian 14.3.0-12) 14.3.0’ Flavor: r-patched-linux-x86_64

Version: 4.11.0
Check: installed package size
Result: NOTE installed size is 8.8Mb sub-directories of 1Mb or more: R 2.0Mb doc 1.3Mb help 1.1Mb libs 3.5Mb Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64

Version: 4.11.0
Flags: --no-examples --no-tests --no-vignettes
Check: installed package size
Result: NOTE installed size is 5.0Mb sub-directories of 1Mb or more: R 1.1Mb doc 1.3Mb help 1.1Mb Flavor: r-oldrel-windows-x86_64

Package ergm.count

Current CRAN status: OK: 14

Package ergm.ego

Current CRAN status: OK: 14

Package ergm.multi

Current CRAN status: OK: 14

Package ergm.rank

Current CRAN status: OK: 14

Package latentnet

Current CRAN status: NOTE: 3, OK: 11

Version: 2.12.0
Check: for GNU extensions in Makefiles
Result: NOTE GNU make is a SystemRequirements. Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64

Package piecemeal

Current CRAN status: OK: 14

Package rle

Current CRAN status: ERROR: 1, OK: 13

Version: 0.10.0
Check: whether package can be installed
Result: ERROR Installation failed. Flavor: r-oldrel-windows-x86_64

Package statnet.common

Current CRAN status: NOTE: 14

Version: 4.13.0
Check: R code for possible problems
Result: NOTE Found the following possibly unsafe calls: File ‘statnet.common/R/control.utilities.R’: unlockBinding("snctrl", environment(snctrl)) unlockBinding("snctrl", environment(update_my_snctrl)) Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-macos-arm64, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64

Package tergm

Current CRAN status: NOTE: 5, OK: 9

Version: 4.2.2
Check: HTML version of manual
Result: NOTE Found the following HTML validation problems: tergm-package.html:223:72 (tergm-package.Rd:133): Warning: missing </p> before <a> tergm-package.html:223:5 (tergm-package.Rd:133): Warning: missing </a> before <a> tergm-package.html:223:192 (tergm-package.Rd:133): Warning: inserting implicit <p> tergm-package.html:223:196 (tergm-package.Rd:133): Warning: discarding unexpected </a> tergm-package.html:223:192 (tergm-package.Rd:133): Warning: trimming empty <p> Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 4.2.2
Flags: --no-vignettes
Check: HTML version of manual
Result: NOTE Found the following HTML validation problems: tergm-package.html:223:60 (tergm-package.Rd:133): Warning: missing </p> before <a> tergm-package.html:223:5 (tergm-package.Rd:133): Warning: missing </a> before <a> tergm-package.html:223:180 (tergm-package.Rd:133): Warning: inserting implicit <p> tergm-package.html:223:184 (tergm-package.Rd:133): Warning: discarding unexpected </a> tergm-package.html:223:180 (tergm-package.Rd:133): Warning: trimming empty <p> Flavor: r-devel-windows-x86_64