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Last updated on 2026-09-29 17:49:21 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-fedora-clang | 1.1.0 | 93.00 | 226.37 | 319.37 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.1.0 | 144.00 | 228.88 | 372.88 | OK | |
| r-release-macos-arm64 | 1.1.0 | 39.00 | 83.00 | 122.00 | OK | |
| r-release-macos-x86_64 | 1.1.0 | 127.00 | 420.00 | 547.00 | ERROR | |
| r-release-windows-x86_64 | 1.1.0 | 218.00 | 360.00 | 578.00 | OK | |
| r-oldrel-macos-arm64 | 1.1.0 | 51.00 | 126.00 | 177.00 | OK | |
| r-oldrel-macos-x86_64 | 1.1.0 | 125.00 | 391.00 | 516.00 | ERROR |
Version: 1.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [33s/46s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(parsnip)
> library(generics)
Attaching package: 'generics'
The following objects are masked from 'package:base':
as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
setequal, union
> library(spsurv)
Loading required package: survival
Loading required package: coda
> veteran <- survival::veteran
>
> test_check("spsurv")
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.2e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.019 seconds (Warm-up)
Chain 1: 0.013 seconds (Sampling)
Chain 1: 0.032 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.01 seconds (Warm-up)
Chain 1: 0.007 seconds (Sampling)
Chain 1: 0.017 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000316 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.012 seconds (Warm-up)
Chain 1: 0.013 seconds (Sampling)
Chain 1: 0.025 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.032 seconds (Warm-up)
Chain 1: 0.019 seconds (Sampling)
Chain 1: 0.051 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.008 seconds (Warm-up)
Chain 1: 0 seconds (Sampling)
Chain 1: 0.008 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.018 seconds (Warm-up)
Chain 1: 0.002 seconds (Sampling)
Chain 1: 0.02 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.038 seconds (Warm-up)
Chain 1: 0.042 seconds (Sampling)
Chain 1: 0.08 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.019 seconds (Warm-up)
Chain 1: 0.022 seconds (Sampling)
Chain 1: 0.041 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.003 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.004 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.2e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.017 seconds (Warm-up)
Chain 1: 0.016 seconds (Sampling)
Chain 1: 0.033 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.014 seconds (Warm-up)
Chain 1: 0.003 seconds (Sampling)
Chain 1: 0.017 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.9e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.013 seconds (Warm-up)
Chain 1: 0.003 seconds (Sampling)
Chain 1: 0.016 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000304 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.077 seconds (Warm-up)
Chain 1: 0.118 seconds (Sampling)
Chain 1: 0.195 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.005 seconds (Warm-up)
Chain 1: 0.004 seconds (Sampling)
Chain 1: 0.009 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.026 seconds (Warm-up)
Chain 1: 0.014 seconds (Sampling)
Chain 1: 0.04 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.007 seconds (Warm-up)
Chain 1: 0.012 seconds (Sampling)
Chain 1: 0.019 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.018 seconds (Warm-up)
Chain 1: 0.009 seconds (Sampling)
Chain 1: 0.027 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.2e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
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Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.01 seconds (Warm-up)
Chain 1: 0.017 seconds (Sampling)
Chain 1: 0.027 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
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Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.015 seconds (Warm-up)
Chain 1: 0.01 seconds (Sampling)
Chain 1: 0.025 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000361 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
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Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.014 seconds (Warm-up)
Chain 1: 0.022 seconds (Sampling)
Chain 1: 0.036 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.01 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.011 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 7.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.78 seconds.
Chain 1: Adjust your expectations accordingly!
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
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Chain 1:
Chain 1:
Chain 1: WARNING: There aren't enough warmup iterations to fit the
Chain 1: three stages of adaptation as currently configured.
Chain 1: Reducing each adaptation stage to 15%/75%/10% of
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.6 seconds.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000322 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.22 seconds.
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: There aren't enough warmup iterations to fit the
Chain 1: three stages of adaptation as currently configured.
Chain 1: Reducing each adaptation stage to 15%/75%/10% of
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
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Saving _problems/test-vcov-130.R
Call:
bpph(formula = Surv(time, status) ~ 1, data = veteran, approach = "mle",
model = "ph")
log(gamma) gamma
gamma[1] -0.029 1.0
gamma[2] -1.286 0.3
gamma[3] -0.052 0.9
gamma[4] -10.817 0.0
gamma[5] -22.955 0.0
gamma[6] 0.620 1.9
gamma[7] -28.896 0.0
gamma[8] -74.862 0.0
gamma[9] -66.742 0.0
gamma[10] -118.725 0.0
gamma[11] -56.079 0.0
gamma[12] 0.054 1.1
Loglik(model)= -744 Loglik(baseline only)= -744
n= 137, number of events= 128Call:
bpph(formula = Surv(time, status) ~ 1, data = veteran, approach = "mle",
model = "ph")
log(gamma) gamma
gamma[1] -0.029 1.0
gamma[2] -1.286 0.3
gamma[3] -0.052 0.9
gamma[4] -10.817 0.0
gamma[5] -22.955 0.0
gamma[6] 0.620 1.9
gamma[7] -28.896 0.0
gamma[8] -74.862 0.0
gamma[9] -66.742 0.0
gamma[10] -118.725 0.0
gamma[11] -56.079 0.0
gamma[12] 0.054 1.1
Loglik(model)= -744 Loglik(baseline only)= -744
n= 137, number of events= 128
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.006 seconds (Warm-up)
Chain 1: 0.002 seconds (Sampling)
Chain 1: 0.008 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 4.2e-05 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: WARNING: No variance estimation is
Chain 2: performed for num_warmup < 20
Chain 2:
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Chain 2:
Chain 2: Elapsed Time: 0.011 seconds (Warm-up)
Chain 2: 0.004 seconds (Sampling)
Chain 2: 0.015 seconds (Total)
Chain 2:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 4.1e-05 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: WARNING: No variance estimation is
Chain 3: performed for num_warmup < 20
Chain 3:
Chain 3: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 3:
Chain 3: Elapsed Time: 0.01 seconds (Warm-up)
Chain 3: 0.008 seconds (Sampling)
Chain 3: 0.018 seconds (Total)
Chain 3:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 5e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.5 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: WARNING: No variance estimation is
Chain 4: performed for num_warmup < 20
Chain 4:
Chain 4: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 4:
Chain 4: Elapsed Time: 0.004 seconds (Warm-up)
Chain 4: 0.024 seconds (Sampling)
Chain 4: 0.028 seconds (Total)
Chain 4:
Call:
bpph(formula = Surv(time, status) ~ 1, data = veteran, approach = "bayes",
iter = 10, cores = 1, model = "ph")
mean(bp) mode(bp) median(bp) mean(log(bp)) sd(bp)
gamma[1] 1.049 1.060 1.052 0.031 0.2
gamma[2] 0.293 0.083 0.137 -2.194 0.3
gamma[3] 0.434 0.192 0.274 -1.304 0.4
gamma[4] 0.500 0.249 0.421 -1.210 0.4
gamma[5] 0.144 0.049 0.117 -2.423 0.1
gamma[6] 0.767 0.333 0.512 -1.104 0.7
gamma[7] 0.664 0.141 0.295 -1.477 0.8
gamma[8] 0.388 0.078 0.070 -2.088 0.6
gamma[9] 0.266 0.058 0.146 -2.774 0.4
gamma[10] 0.157 0.012 0.019 -3.967 0.3
gamma[11] 0.153 0.050 0.107 -2.947 0.2
gamma[12] 1.179 0.864 0.970 -0.024 0.8
Deviance criterion= 1502 Watanabe–Akaike criterion= -750
Log pseudo-marginal lik= -750
n= 137, number of events= 128Call:
bpph(formula = Surv(time, status) ~ 1, data = veteran, approach = "bayes",
iter = 10, cores = 1, model = "ph")
mean(bp) mode(bp) median(bp) mean(log(bp)) sd(bp)
gamma[1] 1.049 1.060 1.052 0.031 0.2
gamma[2] 0.293 0.083 0.137 -2.194 0.3
gamma[3] 0.434 0.192 0.274 -1.304 0.4
gamma[4] 0.500 0.249 0.421 -1.210 0.4
gamma[5] 0.144 0.049 0.117 -2.423 0.1
gamma[6] 0.767 0.333 0.512 -1.104 0.7
gamma[7] 0.664 0.141 0.295 -1.477 0.8
gamma[8] 0.388 0.078 0.070 -2.088 0.6
gamma[9] 0.266 0.058 0.146 -2.774 0.4
gamma[10] 0.157 0.012 0.019 -3.967 0.3
gamma[11] 0.153 0.050 0.107 -2.947 0.2
gamma[12] 1.179 0.864 0.970 -0.024 0.8
Deviance criterion= 1502 Watanabe–Akaike criterion= -750
Log pseudo-marginal lik= -750
n= 137, number of events= 128Call:
bpph(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "ph")
Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0310 -0.0411 -0.0209 0.0052 -6.0 2e-09
factor(celltype)smallcell 0.7280 0.2413 1.2148 0.2483 2.9 0.003
factor(celltype)adeno 1.1330 0.5630 1.7030 0.2908 3.9 1e-04
factor(celltype)large 0.3244 -0.2132 0.8621 0.2743 1.2 0.237
karno ***
factor(celltype)smallcell **
factor(celltype)adeno ***
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1.0
factor(celltype)smallcell 2.07 1.27 3.4
factor(celltype)adeno 3.10 1.76 5.5
factor(celltype)large 1.38 0.81 2.4
---
loglik = -714 AIC = 1460
Call:
bpph(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "ph")
Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0310 -0.0411 -0.0209 0.0052 -6.0 2e-09
factor(celltype)smallcell 0.7280 0.2413 1.2148 0.2483 2.9 0.003
factor(celltype)adeno 1.1330 0.5630 1.7030 0.2908 3.9 1e-04
factor(celltype)large 0.3244 -0.2132 0.8621 0.2743 1.2 0.237
karno ***
factor(celltype)smallcell **
factor(celltype)adeno ***
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1.0
factor(celltype)smallcell 2.07 1.27 3.4
factor(celltype)adeno 3.10 1.76 5.5
factor(celltype)large 1.38 0.81 2.4
---
loglik = -714 AIC = 1460
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
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Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.002 seconds (Warm-up)
Chain 1: 0.022 seconds (Sampling)
Chain 1: 0.024 seconds (Total)
Chain 1:
Call:
bpph(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "ph")
Bayesian Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno -0.029 -0.040 -0.016 0.0
factor(celltype)smallcell 1.216 0.630 2.591 0.8
factor(celltype)adeno 1.564 0.880 3.343 1.0
factor(celltype)large 0.839 0.125 3.195 1.3
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1
factor(celltype)smallcell 4.62 1.88 13
factor(celltype)adeno 8.22 2.41 28
factor(celltype)large 5.92 1.13 24
---
DIC = 10366 WAIC = -953
Call:
bpph(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "ph")
Bayesian Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno -0.029 -0.040 -0.016 0.0
factor(celltype)smallcell 1.216 0.630 2.591 0.8
factor(celltype)adeno 1.564 0.880 3.343 1.0
factor(celltype)large 0.839 0.125 3.195 1.3
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1
factor(celltype)smallcell 4.62 1.88 13
factor(celltype)adeno 8.22 2.41 28
factor(celltype)large 5.92 1.13 24
---
DIC = 10366 WAIC = -953
Call:
bppo(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "po")
Bernstein PO model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0613 -0.0785 -0.0441 0.0088 -7.0 3e-12
factor(celltype)smallcell 1.2884 0.4314 2.1454 0.4372 2.9 0.003
factor(celltype)adeno 1.4378 0.5094 2.3662 0.4737 3.0 0.002
factor(celltype)large 0.1079 -0.8066 1.0223 0.4666 0.2 0.817
karno ***
factor(celltype)smallcell **
factor(celltype)adeno **
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.94 0.92 1.0
factor(celltype)smallcell 3.63 1.54 8.5
factor(celltype)adeno 4.21 1.66 10.7
factor(celltype)large 1.11 0.45 2.8
---
loglik = -708 AIC = 1448
Call:
bppo(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "po")
Bernstein PO model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0613 -0.0785 -0.0441 0.0088 -7.0 3e-12
factor(celltype)smallcell 1.2884 0.4314 2.1454 0.4372 2.9 0.003
factor(celltype)adeno 1.4378 0.5094 2.3662 0.4737 3.0 0.002
factor(celltype)large 0.1079 -0.8066 1.0223 0.4666 0.2 0.817
karno ***
factor(celltype)smallcell **
factor(celltype)adeno **
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.94 0.92 1.0
factor(celltype)smallcell 3.63 1.54 8.5
factor(celltype)adeno 4.21 1.66 10.7
factor(celltype)large 1.11 0.45 2.8
---
loglik = -708 AIC = 1448
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 8.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
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Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.027 seconds (Warm-up)
Chain 1: 0.019 seconds (Sampling)
Chain 1: 0.046 seconds (Total)
Chain 1:
Call:
bppo(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "po")
Bayesian Bernstein PO model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno -0.074 -0.076 -0.067 0.0
factor(celltype)smallcell 0.931 0.661 0.999 0.2
factor(celltype)adeno 0.825 0.806 0.901 0.0
factor(celltype)large -0.462 -0.572 -0.434 0.1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.93 0.93 0.9
factor(celltype)smallcell 2.56 1.94 2.7
factor(celltype)adeno 2.28 2.24 2.5
factor(celltype)large 0.63 0.56 0.6
---
DIC = 1428 WAIC = -714
Call:
bppo(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "po")
Bayesian Bernstein PO model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno -0.074 -0.076 -0.067 0.0
factor(celltype)smallcell 0.931 0.661 0.999 0.2
factor(celltype)adeno 0.825 0.806 0.901 0.0
factor(celltype)large -0.462 -0.572 -0.434 0.1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.93 0.93 0.9
factor(celltype)smallcell 2.56 1.94 2.7
factor(celltype)adeno 2.28 2.24 2.5
factor(celltype)large 0.63 0.56 0.6
---
DIC = 1428 WAIC = -714
Call:
bpaft(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "aft")
Bernstein AFT model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno 0.0347 0.0252 0.0443 0.0049 7.1 1e-12
factor(celltype)smallcell -0.7481 -1.3137 -0.1825 0.2886 -2.6 0.010
factor(celltype)adeno -0.9000 -1.4563 -0.3438 0.2838 -3.2 0.002
factor(celltype)large -0.1338 -0.6885 0.4208 0.2830 -0.5 0.636
karno ***
factor(celltype)smallcell **
factor(celltype)adeno **
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.04 1.03 1.0
factor(celltype)smallcell 0.47 0.27 0.8
factor(celltype)adeno 0.41 0.23 0.7
factor(celltype)large 0.87 0.50 1.5
---
loglik = -710 AIC = 1451
Call:
bpaft(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "aft")
Bernstein AFT model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno 0.0347 0.0252 0.0443 0.0049 7.1 1e-12
factor(celltype)smallcell -0.7481 -1.3137 -0.1825 0.2886 -2.6 0.010
factor(celltype)adeno -0.9000 -1.4563 -0.3438 0.2838 -3.2 0.002
factor(celltype)large -0.1338 -0.6885 0.4208 0.2830 -0.5 0.636
karno ***
factor(celltype)smallcell **
factor(celltype)adeno **
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.04 1.03 1.0
factor(celltype)smallcell 0.47 0.27 0.8
factor(celltype)adeno 0.41 0.23 0.7
factor(celltype)large 0.87 0.50 1.5
---
loglik = -710 AIC = 1451
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.001126 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
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Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.005 seconds (Warm-up)
Chain 1: 0.042 seconds (Sampling)
Chain 1: 0.047 seconds (Total)
Chain 1:
Call:
bpaft(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "aft")
Bayesian Bernstein AFT model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno 0.043 0.039 0.048 0.0
factor(celltype)smallcell 0.233 -0.048 0.402 0.2
factor(celltype)adeno 1.075 0.745 1.276 0.3
factor(celltype)large 2.384 1.639 2.856 0.4
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.04 1.04 1.0
factor(celltype)smallcell 1.29 0.95 1.5
factor(celltype)adeno 3.02 2.11 3.6
factor(celltype)large 11.64 5.15 17.4
---
DIC = 1527 WAIC = -758
Call:
bpaft(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "aft")
Bayesian Bernstein AFT model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno 0.043 0.039 0.048 0.0
factor(celltype)smallcell 0.233 -0.048 0.402 0.2
factor(celltype)adeno 1.075 0.745 1.276 0.3
factor(celltype)large 2.384 1.639 2.856 0.4
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.04 1.04 1.0
factor(celltype)smallcell 1.29 0.95 1.5
factor(celltype)adeno 3.02 2.11 3.6
factor(celltype)large 11.64 5.15 17.4
---
DIC = 1527 WAIC = -758
Call:
spbp.default(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "ph", degree = 12)
Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0310 -0.0411 -0.0209 0.0052 -6.0 2e-09
factor(celltype)smallcell 0.7276 0.2400 1.2152 0.2488 2.9 0.003
factor(celltype)adeno 1.1328 0.5623 1.7033 0.2911 3.9 1e-04
factor(celltype)large 0.3242 -0.2138 0.8622 0.2745 1.2 0.238
karno ***
factor(celltype)smallcell **
factor(celltype)adeno ***
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1.0
factor(celltype)smallcell 2.07 1.27 3.4
factor(celltype)adeno 3.10 1.75 5.5
factor(celltype)large 1.38 0.81 2.4
---
loglik = -714 AIC = 1460
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
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Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0 seconds (Warm-up)
Chain 1: 0 seconds (Sampling)
Chain 1: 0 seconds (Total)
Chain 1:
Call:
spbp.default(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", cores = 1, chains = 1,
iter = 10, model = "ph", degree = 12)
Bayesian Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno 0.024 0.024 0.024 0
factor(celltype)smallcell -0.613 -0.613 -0.613 0
factor(celltype)adeno -4.014 -4.014 -4.014 0
factor(celltype)large -2.334 -2.334 -2.334 0
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.025 1.025 1.0
factor(celltype)smallcell 0.542 0.542 0.5
factor(celltype)adeno 0.018 0.018 0.0
factor(celltype)large 0.097 0.097 0.1
---
DIC = 2885 WAIC = -1443
[ FAIL 3 | WARN 46 | SKIP 1 | PASS 672 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• Set SPSURV_RUN_COVERAGE=true to run coverage check (1): 'test-coverage.R:6:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-vcov.R:127:3'): ill-conditioned gamma block still returns survfit uncertainty with warning ──
Error: symmetric inverse requires a finite numeric matrix
Backtrace:
▆
1. ├─testthat::expect_warning(...) at test-vcov.R:127:3
2. │ └─testthat:::quasi_capture(...)
3. │ ├─testthat (local) .capture(...)
4. │ │ └─base::withCallingHandlers(...)
5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
6. ├─survival::survfit(fit, times = c(10, 50, 100))
7. └─spsurv:::survfit.spbp(fit, times = c(10, 50, 100))
8. └─spsurv:::.spbp_eval_survival(...)
9. ├─stats::vcov(x, bp.param = TRUE, mask_unstable_gamma = FALSE)
10. └─spsurv:::vcov.spbp(x, bp.param = TRUE, mask_unstable_gamma = FALSE)
11. └─spsurv:::.spbp_sym_inv(Schur_A)
── Error ('test-vcov.R:149:3'): ill-conditioned gamma block NA only gamma variances in full vcov ──
Error: symmetric inverse requires a finite numeric matrix
Backtrace:
▆
1. ├─testthat::expect_silent(v_full <- vcov(fit, bp.param = TRUE)) at test-vcov.R:149:3
2. │ └─testthat:::quasi_capture(enquo(object), NULL, evaluate_promise)
3. │ ├─testthat (local) .capture(...)
4. │ │ ├─withr::with_output_sink(...)
5. │ │ │ └─base::force(code)
6. │ │ ├─base::withCallingHandlers(...)
7. │ │ └─base::withVisible(code)
8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
9. ├─stats::vcov(fit, bp.param = TRUE)
10. └─spsurv:::vcov.spbp(fit, bp.param = TRUE)
11. └─spsurv:::.spbp_sym_inv(Schur_A)
── Error ('test-vcov.R:166:3'): ill-conditioned gamma warning appears only for survfit, not after fit ──
Error: symmetric inverse requires a finite numeric matrix
Backtrace:
▆
1. ├─testthat::expect_silent(vcov(fit)) at test-vcov.R:166:3
2. │ └─testthat:::quasi_capture(enquo(object), NULL, evaluate_promise)
3. │ ├─testthat (local) .capture(...)
4. │ │ ├─withr::with_output_sink(...)
5. │ │ │ └─base::force(code)
6. │ │ ├─base::withCallingHandlers(...)
7. │ │ └─base::withVisible(code)
8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
9. ├─stats::vcov(fit)
10. └─spsurv:::vcov.spbp(fit)
11. └─spsurv:::.spbp_sym_inv(Schur_A)
[ FAIL 3 | WARN 46 | SKIP 1 | PASS 672 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-macos-x86_64
Version: 1.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [31s/51s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(parsnip)
> library(generics)
Attaching package: 'generics'
The following objects are masked from 'package:base':
as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
setequal, union
> library(spsurv)
Loading required package: survival
Loading required package: coda
> veteran <- survival::veteran
>
> test_check("spsurv")
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.2e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
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Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.022 seconds (Warm-up)
Chain 1: 0.016 seconds (Sampling)
Chain 1: 0.038 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.015 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.016 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000328 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.006 seconds (Warm-up)
Chain 1: 0.016 seconds (Sampling)
Chain 1: 0.022 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.014 seconds (Warm-up)
Chain 1: 0.005 seconds (Sampling)
Chain 1: 0.019 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.022 seconds (Warm-up)
Chain 1: 0.024 seconds (Sampling)
Chain 1: 0.046 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.018 seconds (Warm-up)
Chain 1: 0.022 seconds (Sampling)
Chain 1: 0.04 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.018 seconds (Warm-up)
Chain 1: 0.014 seconds (Sampling)
Chain 1: 0.032 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.016 seconds (Warm-up)
Chain 1: 0.022 seconds (Sampling)
Chain 1: 0.038 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.007 seconds (Warm-up)
Chain 1: 0.019 seconds (Sampling)
Chain 1: 0.026 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.007 seconds (Warm-up)
Chain 1: 0 seconds (Sampling)
Chain 1: 0.007 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.009 seconds (Warm-up)
Chain 1: 0 seconds (Sampling)
Chain 1: 0.009 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.027 seconds (Warm-up)
Chain 1: 0.016 seconds (Sampling)
Chain 1: 0.043 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000311 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.043 seconds (Warm-up)
Chain 1: 0.107 seconds (Sampling)
Chain 1: 0.15 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.032 seconds (Warm-up)
Chain 1: 0.019 seconds (Sampling)
Chain 1: 0.051 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.006 seconds (Warm-up)
Chain 1: 0.018 seconds (Sampling)
Chain 1: 0.024 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.021 seconds (Warm-up)
Chain 1: 0.011 seconds (Sampling)
Chain 1: 0.032 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.009 seconds (Warm-up)
Chain 1: 0.014 seconds (Sampling)
Chain 1: 0.023 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.014 seconds (Warm-up)
Chain 1: 0.006 seconds (Sampling)
Chain 1: 0.02 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
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Chain 1: performed for num_warmup < 20
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Chain 1:
Chain 1: Elapsed Time: 0.021 seconds (Warm-up)
Chain 1: 0.019 seconds (Sampling)
Chain 1: 0.04 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000308 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.433 seconds (Warm-up)
Chain 1: 0.19 seconds (Sampling)
Chain 1: 0.623 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.015 seconds (Warm-up)
Chain 1: 0.009 seconds (Sampling)
Chain 1: 0.024 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.027 seconds (Warm-up)
Chain 1: 0.017 seconds (Sampling)
Chain 1: 0.044 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.018 seconds (Warm-up)
Chain 1: 0.003 seconds (Sampling)
Chain 1: 0.021 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.025 seconds (Warm-up)
Chain 1: 0.014 seconds (Sampling)
Chain 1: 0.039 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.015 seconds (Warm-up)
Chain 1: 0.018 seconds (Sampling)
Chain 1: 0.033 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.024 seconds (Warm-up)
Chain 1: 0.004 seconds (Sampling)
Chain 1: 0.028 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.025 seconds (Warm-up)
Chain 1: 0.021 seconds (Sampling)
Chain 1: 0.046 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: There aren't enough warmup iterations to fit the
Chain 1: three stages of adaptation as currently configured.
Chain 1: Reducing each adaptation stage to 15%/75%/10% of
Chain 1: the given number of warmup iterations:
Chain 1: init_buffer = 3
Chain 1: adapt_window = 20
Chain 1: term_buffer = 2
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.631 seconds (Warm-up)
Chain 1: 0.666 seconds (Sampling)
Chain 1: 1.297 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.016 seconds (Warm-up)
Chain 1: 0.008 seconds (Sampling)
Chain 1: 0.024 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.017 seconds (Warm-up)
Chain 1: 0.013 seconds (Sampling)
Chain 1: 0.03 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.017 seconds (Warm-up)
Chain 1: 0.013 seconds (Sampling)
Chain 1: 0.03 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.01 seconds (Warm-up)
Chain 1: 0.004 seconds (Sampling)
Chain 1: 0.014 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.00032 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.491 seconds (Warm-up)
Chain 1: 0.274 seconds (Sampling)
Chain 1: 0.765 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.019 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.02 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.016 seconds (Warm-up)
Chain 1: 0.012 seconds (Sampling)
Chain 1: 0.028 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.025 seconds (Warm-up)
Chain 1: 0.004 seconds (Sampling)
Chain 1: 0.029 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.9e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: There aren't enough warmup iterations to fit the
Chain 1: three stages of adaptation as currently configured.
Chain 1: Reducing each adaptation stage to 15%/75%/10% of
Chain 1: the given number of warmup iterations:
Chain 1: init_buffer = 3
Chain 1: adapt_window = 15
Chain 1: term_buffer = 2
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Chain 1:
Chain 1: Elapsed Time: 0.252 seconds (Warm-up)
Chain 1: 0.185 seconds (Sampling)
Chain 1: 0.437 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.03 seconds (Warm-up)
Chain 1: 0.048 seconds (Sampling)
Chain 1: 0.078 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
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Chain 1:
Chain 1: Elapsed Time: 0.04 seconds (Warm-up)
Chain 1: 0.109 seconds (Sampling)
Chain 1: 0.149 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
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Chain 1:
Chain 1: Elapsed Time: 0.11 seconds (Warm-up)
Chain 1: 0.063 seconds (Sampling)
Chain 1: 0.173 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
Chain 1: Adjust your expectations accordingly!
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Saving _problems/test-vcov-130.R
Call:
bpph(formula = Surv(time, status) ~ 1, data = veteran, approach = "mle",
model = "ph")
log(gamma) gamma
gamma[1] -0.029 1.0
gamma[2] -1.286 0.3
gamma[3] -0.052 0.9
gamma[4] -10.817 0.0
gamma[5] -22.955 0.0
gamma[6] 0.620 1.9
gamma[7] -28.896 0.0
gamma[8] -74.862 0.0
gamma[9] -66.742 0.0
gamma[10] -118.725 0.0
gamma[11] -56.079 0.0
gamma[12] 0.054 1.1
Loglik(model)= -744 Loglik(baseline only)= -744
n= 137, number of events= 128Call:
bpph(formula = Surv(time, status) ~ 1, data = veteran, approach = "mle",
model = "ph")
log(gamma) gamma
gamma[1] -0.029 1.0
gamma[2] -1.286 0.3
gamma[3] -0.052 0.9
gamma[4] -10.817 0.0
gamma[5] -22.955 0.0
gamma[6] 0.620 1.9
gamma[7] -28.896 0.0
gamma[8] -74.862 0.0
gamma[9] -66.742 0.0
gamma[10] -118.725 0.0
gamma[11] -56.079 0.0
gamma[12] 0.054 1.1
Loglik(model)= -744 Loglik(baseline only)= -744
n= 137, number of events= 128
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 3.9e-05 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 4e-05 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
Chain 3: Adjust your expectations accordingly!
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 4e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
Chain 4: Adjust your expectations accordingly!
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Call:
bpph(formula = Surv(time, status) ~ 1, data = veteran, approach = "bayes",
iter = 10, cores = 1, model = "ph")
mean(bp) mode(bp) median(bp) mean(log(bp)) sd(bp)
gamma[1] 1.049 1.060 1.052 0.031 0.2
gamma[2] 0.293 0.083 0.137 -2.194 0.3
gamma[3] 0.434 0.192 0.274 -1.304 0.4
gamma[4] 0.500 0.249 0.421 -1.210 0.4
gamma[5] 0.144 0.049 0.117 -2.423 0.1
gamma[6] 0.767 0.333 0.512 -1.104 0.7
gamma[7] 0.664 0.141 0.295 -1.477 0.8
gamma[8] 0.388 0.078 0.070 -2.088 0.6
gamma[9] 0.266 0.058 0.146 -2.774 0.4
gamma[10] 0.157 0.012 0.019 -3.967 0.3
gamma[11] 0.153 0.050 0.107 -2.947 0.2
gamma[12] 1.179 0.864 0.970 -0.024 0.8
Deviance criterion= 1502 Watanabe–Akaike criterion= -750
Log pseudo-marginal lik= -750
n= 137, number of events= 128Call:
bpph(formula = Surv(time, status) ~ 1, data = veteran, approach = "bayes",
iter = 10, cores = 1, model = "ph")
mean(bp) mode(bp) median(bp) mean(log(bp)) sd(bp)
gamma[1] 1.049 1.060 1.052 0.031 0.2
gamma[2] 0.293 0.083 0.137 -2.194 0.3
gamma[3] 0.434 0.192 0.274 -1.304 0.4
gamma[4] 0.500 0.249 0.421 -1.210 0.4
gamma[5] 0.144 0.049 0.117 -2.423 0.1
gamma[6] 0.767 0.333 0.512 -1.104 0.7
gamma[7] 0.664 0.141 0.295 -1.477 0.8
gamma[8] 0.388 0.078 0.070 -2.088 0.6
gamma[9] 0.266 0.058 0.146 -2.774 0.4
gamma[10] 0.157 0.012 0.019 -3.967 0.3
gamma[11] 0.153 0.050 0.107 -2.947 0.2
gamma[12] 1.179 0.864 0.970 -0.024 0.8
Deviance criterion= 1502 Watanabe–Akaike criterion= -750
Log pseudo-marginal lik= -750
n= 137, number of events= 128Call:
bpph(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "ph")
Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0310 -0.0411 -0.0209 0.0052 -6.0 2e-09
factor(celltype)smallcell 0.7280 0.2413 1.2148 0.2483 2.9 0.003
factor(celltype)adeno 1.1330 0.5630 1.7030 0.2908 3.9 1e-04
factor(celltype)large 0.3244 -0.2132 0.8621 0.2743 1.2 0.237
karno ***
factor(celltype)smallcell **
factor(celltype)adeno ***
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1.0
factor(celltype)smallcell 2.07 1.27 3.4
factor(celltype)adeno 3.10 1.76 5.5
factor(celltype)large 1.38 0.81 2.4
---
loglik = -714 AIC = 1460
Call:
bpph(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "ph")
Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0310 -0.0411 -0.0209 0.0052 -6.0 2e-09
factor(celltype)smallcell 0.7280 0.2413 1.2148 0.2483 2.9 0.003
factor(celltype)adeno 1.1330 0.5630 1.7030 0.2908 3.9 1e-04
factor(celltype)large 0.3244 -0.2132 0.8621 0.2743 1.2 0.237
karno ***
factor(celltype)smallcell **
factor(celltype)adeno ***
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1.0
factor(celltype)smallcell 2.07 1.27 3.4
factor(celltype)adeno 3.10 1.76 5.5
factor(celltype)large 1.38 0.81 2.4
---
loglik = -714 AIC = 1460
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.001 seconds (Warm-up)
Chain 1: 0.026 seconds (Sampling)
Chain 1: 0.027 seconds (Total)
Chain 1:
Call:
bpph(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "ph")
Bayesian Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno -0.029 -0.040 -0.016 0.0
factor(celltype)smallcell 1.216 0.630 2.591 0.8
factor(celltype)adeno 1.564 0.880 3.343 1.0
factor(celltype)large 0.839 0.125 3.195 1.3
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1
factor(celltype)smallcell 4.62 1.88 13
factor(celltype)adeno 8.22 2.41 28
factor(celltype)large 5.92 1.13 24
---
DIC = 10366 WAIC = -953
Call:
bpph(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "ph")
Bayesian Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno -0.029 -0.040 -0.016 0.0
factor(celltype)smallcell 1.216 0.630 2.591 0.8
factor(celltype)adeno 1.564 0.880 3.343 1.0
factor(celltype)large 0.839 0.125 3.195 1.3
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1
factor(celltype)smallcell 4.62 1.88 13
factor(celltype)adeno 8.22 2.41 28
factor(celltype)large 5.92 1.13 24
---
DIC = 10366 WAIC = -953
Call:
bppo(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "po")
Bernstein PO model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0613 -0.0785 -0.0441 0.0088 -7.0 3e-12
factor(celltype)smallcell 1.2884 0.4314 2.1454 0.4372 2.9 0.003
factor(celltype)adeno 1.4378 0.5094 2.3662 0.4737 3.0 0.002
factor(celltype)large 0.1079 -0.8066 1.0223 0.4666 0.2 0.817
karno ***
factor(celltype)smallcell **
factor(celltype)adeno **
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.94 0.92 1.0
factor(celltype)smallcell 3.63 1.54 8.5
factor(celltype)adeno 4.21 1.66 10.7
factor(celltype)large 1.11 0.45 2.8
---
loglik = -708 AIC = 1448
Call:
bppo(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "po")
Bernstein PO model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0613 -0.0785 -0.0441 0.0088 -7.0 3e-12
factor(celltype)smallcell 1.2884 0.4314 2.1454 0.4372 2.9 0.003
factor(celltype)adeno 1.4378 0.5094 2.3662 0.4737 3.0 0.002
factor(celltype)large 0.1079 -0.8066 1.0223 0.4666 0.2 0.817
karno ***
factor(celltype)smallcell **
factor(celltype)adeno **
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.94 0.92 1.0
factor(celltype)smallcell 3.63 1.54 8.5
factor(celltype)adeno 4.21 1.66 10.7
factor(celltype)large 1.11 0.45 2.8
---
loglik = -708 AIC = 1448
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 0.019 seconds (Warm-up)
Chain 1: 0.021 seconds (Sampling)
Chain 1: 0.04 seconds (Total)
Chain 1:
Call:
bppo(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "po")
Bayesian Bernstein PO model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno -0.074 -0.076 -0.067 0.0
factor(celltype)smallcell 0.931 0.661 0.999 0.2
factor(celltype)adeno 0.825 0.806 0.901 0.0
factor(celltype)large -0.462 -0.572 -0.434 0.1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.93 0.93 0.9
factor(celltype)smallcell 2.56 1.94 2.7
factor(celltype)adeno 2.28 2.24 2.5
factor(celltype)large 0.63 0.56 0.6
---
DIC = 1428 WAIC = -714
Call:
bppo(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "po")
Bayesian Bernstein PO model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno -0.074 -0.076 -0.067 0.0
factor(celltype)smallcell 0.931 0.661 0.999 0.2
factor(celltype)adeno 0.825 0.806 0.901 0.0
factor(celltype)large -0.462 -0.572 -0.434 0.1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.93 0.93 0.9
factor(celltype)smallcell 2.56 1.94 2.7
factor(celltype)adeno 2.28 2.24 2.5
factor(celltype)large 0.63 0.56 0.6
---
DIC = 1428 WAIC = -714
Call:
bpaft(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "aft")
Bernstein AFT model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno 0.0347 0.0252 0.0443 0.0049 7.1 1e-12
factor(celltype)smallcell -0.7481 -1.3137 -0.1825 0.2886 -2.6 0.010
factor(celltype)adeno -0.9000 -1.4563 -0.3438 0.2838 -3.2 0.002
factor(celltype)large -0.1338 -0.6885 0.4208 0.2830 -0.5 0.636
karno ***
factor(celltype)smallcell **
factor(celltype)adeno **
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.04 1.03 1.0
factor(celltype)smallcell 0.47 0.27 0.8
factor(celltype)adeno 0.41 0.23 0.7
factor(celltype)large 0.87 0.50 1.5
---
loglik = -710 AIC = 1451
Call:
bpaft(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "aft")
Bernstein AFT model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno 0.0347 0.0252 0.0443 0.0049 7.1 1e-12
factor(celltype)smallcell -0.7481 -1.3137 -0.1825 0.2886 -2.6 0.010
factor(celltype)adeno -0.9000 -1.4563 -0.3438 0.2838 -3.2 0.002
factor(celltype)large -0.1338 -0.6885 0.4208 0.2830 -0.5 0.636
karno ***
factor(celltype)smallcell **
factor(celltype)adeno **
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.04 1.03 1.0
factor(celltype)smallcell 0.47 0.27 0.8
factor(celltype)adeno 0.41 0.23 0.7
factor(celltype)large 0.87 0.50 1.5
---
loglik = -710 AIC = 1451
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.00034 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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Chain 1:
Chain 1: Elapsed Time: 0.006 seconds (Warm-up)
Chain 1: 0.039 seconds (Sampling)
Chain 1: 0.045 seconds (Total)
Chain 1:
Call:
bpaft(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "aft")
Bayesian Bernstein AFT model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno 0.043 0.039 0.048 0.0
factor(celltype)smallcell 0.233 -0.048 0.402 0.2
factor(celltype)adeno 1.075 0.745 1.276 0.3
factor(celltype)large 2.384 1.639 2.856 0.4
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.04 1.04 1.0
factor(celltype)smallcell 1.29 0.95 1.5
factor(celltype)adeno 3.02 2.11 3.6
factor(celltype)large 11.64 5.15 17.4
---
DIC = 1527 WAIC = -758
Call:
bpaft(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", iter = 10, chains = 1,
cores = 1, model = "aft")
Bayesian Bernstein AFT model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno 0.043 0.039 0.048 0.0
factor(celltype)smallcell 0.233 -0.048 0.402 0.2
factor(celltype)adeno 1.075 0.745 1.276 0.3
factor(celltype)large 2.384 1.639 2.856 0.4
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.04 1.04 1.0
factor(celltype)smallcell 1.29 0.95 1.5
factor(celltype)adeno 3.02 2.11 3.6
factor(celltype)large 11.64 5.15 17.4
---
DIC = 1527 WAIC = -758
Call:
spbp.default(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "mle", model = "ph", degree = 12)
Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error z value Pr(>|z|)
karno -0.0310 -0.0411 -0.0209 0.0052 -6.0 2e-09
factor(celltype)smallcell 0.7276 0.2400 1.2152 0.2488 2.9 0.003
factor(celltype)adeno 1.1328 0.5623 1.7033 0.2911 3.9 1e-04
factor(celltype)large 0.3242 -0.2138 0.8622 0.2745 1.2 0.238
karno ***
factor(celltype)smallcell **
factor(celltype)adeno ***
factor(celltype)large
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 0.97 0.96 1.0
factor(celltype)smallcell 2.07 1.27 3.4
factor(celltype)adeno 3.10 1.75 5.5
factor(celltype)large 1.38 0.81 2.4
---
loglik = -714 AIC = 1460
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1: performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
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Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0 seconds (Warm-up)
Chain 1: 0 seconds (Sampling)
Chain 1: 0 seconds (Total)
Chain 1:
Call:
spbp.default(formula = Surv(time, status) ~ karno + factor(celltype),
data = veteran, approach = "bayes", cores = 1, chains = 1,
iter = 10, model = "ph", degree = 12)
Bayesian Bernstein PH model:
Regression coefficients:
Estimate 2.5% 97.5% Std. Error
karno 0.024 0.024 0.024 0
factor(celltype)smallcell -0.613 -0.613 -0.613 0
factor(celltype)adeno -4.014 -4.014 -4.014 0
factor(celltype)large -2.334 -2.334 -2.334 0
Exponentiated coefficients:
Estimate 2.5% 97.5%
karno 1.025 1.025 1.0
factor(celltype)smallcell 0.542 0.542 0.5
factor(celltype)adeno 0.018 0.018 0.0
factor(celltype)large 0.097 0.097 0.1
---
DIC = 2885 WAIC = -1443
[ FAIL 3 | WARN 46 | SKIP 1 | PASS 672 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• Set SPSURV_RUN_COVERAGE=true to run coverage check (1): 'test-coverage.R:6:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-vcov.R:127:3'): ill-conditioned gamma block still returns survfit uncertainty with warning ──
Error: symmetric inverse requires a finite numeric matrix
Backtrace:
▆
1. ├─testthat::expect_warning(...) at test-vcov.R:127:3
2. │ └─testthat:::quasi_capture(...)
3. │ ├─testthat (local) .capture(...)
4. │ │ └─base::withCallingHandlers(...)
5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
6. ├─survival::survfit(fit, times = c(10, 50, 100))
7. └─spsurv:::survfit.spbp(fit, times = c(10, 50, 100))
8. └─spsurv:::.spbp_eval_survival(...)
9. ├─stats::vcov(x, bp.param = TRUE, mask_unstable_gamma = FALSE)
10. └─spsurv:::vcov.spbp(x, bp.param = TRUE, mask_unstable_gamma = FALSE)
11. └─spsurv:::.spbp_sym_inv(Schur_A)
── Error ('test-vcov.R:149:3'): ill-conditioned gamma block NA only gamma variances in full vcov ──
Error: symmetric inverse requires a finite numeric matrix
Backtrace:
▆
1. ├─testthat::expect_silent(v_full <- vcov(fit, bp.param = TRUE)) at test-vcov.R:149:3
2. │ └─testthat:::quasi_capture(enquo(object), NULL, evaluate_promise)
3. │ ├─testthat (local) .capture(...)
4. │ │ ├─withr::with_output_sink(...)
5. │ │ │ └─base::force(code)
6. │ │ ├─base::withCallingHandlers(...)
7. │ │ └─base::withVisible(code)
8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
9. ├─stats::vcov(fit, bp.param = TRUE)
10. └─spsurv:::vcov.spbp(fit, bp.param = TRUE)
11. └─spsurv:::.spbp_sym_inv(Schur_A)
── Error ('test-vcov.R:166:3'): ill-conditioned gamma warning appears only for survfit, not after fit ──
Error: symmetric inverse requires a finite numeric matrix
Backtrace:
▆
1. ├─testthat::expect_silent(vcov(fit)) at test-vcov.R:166:3
2. │ └─testthat:::quasi_capture(enquo(object), NULL, evaluate_promise)
3. │ ├─testthat (local) .capture(...)
4. │ │ ├─withr::with_output_sink(...)
5. │ │ │ └─base::force(code)
6. │ │ ├─base::withCallingHandlers(...)
7. │ │ └─base::withVisible(code)
8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
9. ├─stats::vcov(fit)
10. └─spsurv:::vcov.spbp(fit)
11. └─spsurv:::.spbp_sym_inv(Schur_A)
[ FAIL 3 | WARN 46 | SKIP 1 | PASS 672 ]
Error:
! Test failures.
Execution halted
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