Last updated on 2026-10-24 01:51:47 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.1.0 | 153.94 | 348.40 | 502.34 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.1.0 | 134.81 | 245.71 | 380.52 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.1.0 | 99.00 | 304.40 | 403.40 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.1.0 | 148.00 | 230.16 | 378.16 | OK | |
| r-devel-windows-x86_64 | 1.1.0 | 204.00 | 443.00 | 647.00 | OK | |
| r-patched-linux-x86_64 | 1.1.0 | 177.83 | 341.83 | 519.66 | OK | |
| r-release-linux-x86_64 | 1.1.0 | 173.44 | 343.40 | 516.84 | OK | |
| r-release-macos-arm64 | 1.1.0 | 39.00 | 83.00 | 122.00 | OK | |
| r-release-macos-x86_64 | 1.1.0 | 131.00 | 665.00 | 796.00 | ERROR | |
| r-release-windows-x86_64 | 1.1.0 | 201.00 | 392.00 | 593.00 | OK | |
| r-oldrel-macos-arm64 | 1.1.0 | 51.00 | 126.00 | 177.00 | OK | |
| r-oldrel-macos-x86_64 | 1.1.0 | 129.00 | 513.00 | 642.00 | ERROR | |
| r-oldrel-windows-x86_64 | 1.1.0 | 270.00 | 481.00 | 751.00 | OK |
Version: 1.1.0
Check: examples
Result: ERROR
Running examples in ‘spsurv-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: predict.spbp
> ### Title: Predicted survival as a tidy data frame
> ### Aliases: predict.spbp
>
> ### ** Examples
>
> data(veteran, package = "survival")
Warning in data(veteran, package = "survival") :
data set ‘veteran’ not found
> fit <- bpph(Surv(time, status) ~ karno, data = veteran, approach = "mle", init = 0)
> pr <- predict(fit, times = seq(0, 400, by = 2))
Error in a$specials[[i]] : subscript out of bounds
Calls: predict ... survfit.coxph -> model.matrix.coxph -> delete.response -> off
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [31s/41s]
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 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
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Chain 1:
Chain 1: Elapsed Time: 0.013 seconds (Warm-up)
Chain 1: 0.004 seconds (Sampling)
Chain 1: 0.017 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
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Chain 1:
Chain 1: Elapsed Time: 0.007 seconds (Warm-up)
Chain 1: 0.002 seconds (Sampling)
Chain 1: 0.009 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000344 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.44 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:
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Chain 1:
Chain 1: Elapsed Time: 0.005 seconds (Warm-up)
Chain 1: 0.006 seconds (Sampling)
Chain 1: 0.011 seconds (Total)
Chain 1:
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)
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Chain 1:
Chain 1: Elapsed Time: 0.012 seconds (Warm-up)
Chain 1: 0.003 seconds (Sampling)
Chain 1: 0.015 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 7.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.74 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: 0.005 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.006 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 7.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.73 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 4.5e-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: 0.012 seconds (Warm-up)
Chain 1: 0.006 seconds (Sampling)
Chain 1: 0.018 seconds (Total)
Chain 1:
Saving _problems/test-coverage-gaps-26.R
Saving _problems/test-coverage-gaps-62.R
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 7.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.75 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: 0.01 seconds (Sampling)
Chain 1: 0.016 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: 0.004 seconds (Warm-up)
Chain 1: 0.002 seconds (Sampling)
Chain 1: 0.006 seconds (Total)
Chain 1:
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
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Chain 1:
Chain 1: Elapsed Time: 0.016 seconds (Warm-up)
Chain 1: 0.003 seconds (Sampling)
Chain 1: 0.019 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.53 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: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.009 seconds (Warm-up)
Chain 1: 0.008 seconds (Sampling)
Chain 1: 0.017 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 7.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.75 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: 0.005 seconds (Sampling)
Chain 1: 0.012 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000552 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.52 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: 0.128 seconds (Warm-up)
Chain 1: 0.201 seconds (Sampling)
Chain 1: 0.329 seconds (Total)
Chain 1:
Saving _problems/test-coverage-gaps-174.R
Saving _problems/test-coverage-gaps-184.R
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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Chain 1:
Chain 1: Elapsed Time: 0.007 seconds (Warm-up)
Chain 1: 0.004 seconds (Sampling)
Chain 1: 0.011 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.57 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: 0.01 seconds (Warm-up)
Chain 1: 0 seconds (Sampling)
Chain 1: 0.01 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.68 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 seconds (Sampling)
Chain 1: 0.006 seconds (Total)
Chain 1:
Saving _problems/test-coverage-gaps-303.R
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: 0.014 seconds (Warm-up)
Chain 1: 0.009 seconds (Sampling)
Chain 1: 0.023 seconds (Total)
Chain 1:
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:
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Chain 1:
Chain 1: Elapsed Time: 0.013 seconds (Warm-up)
Chain 1: 0.011 seconds (Sampling)
Chain 1: 0.024 seconds (Total)
Chain 1:
Saving _problems/test-coverage-gaps-427.R
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.2e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.52 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: 0.01 seconds (Warm-up)
Chain 1: 0.005 seconds (Sampling)
Chain 1: 0.015 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000265 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
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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.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.2e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
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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.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
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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:
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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:
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 7.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
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Chain 1: 0.009 seconds (Total)
Chain 1:
Saving _problems/test-model-comparison-214.R
Saving _problems/test-parsnip-21.R
Saving _problems/test-parsnip-44.R
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
Chain 1: the given number of warmup iterations:
Chain 1: init_buffer = 3
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Chain 1:
Saving _problems/test-predict-censored-5.R
Saving _problems/test-predict-censored-13.R
Saving _problems/test-predict-censored-27.R
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:
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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.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
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Chain 1: 0.014 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:
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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:
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000329 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
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Chain 1: 0.383 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:
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Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.2e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.52 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 5.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
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Chain 1:
Saving _problems/test-survfit-variance-gradients-130.R
Saving _problems/test-survfit-variance-gradients-190.R
Saving _problems/test-survfit-variance-gradients-230.R
Saving _problems/test-survfit-variance-gradients-238.R
Saving _problems/test-survfit-9.R
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: 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
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Chain 1:
Saving _problems/test-survfit-29.R
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Saving _problems/test-survfit-78.R
Saving _problems/test-survfit-91.R
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Saving _problems/test-survfit-115.R
Saving _problems/test-survfit-123.R
Saving _problems/test-survfit-161.R
Saving _problems/test-survfit-170.R
Saving _problems/test-survfit-178.R
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:
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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.
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.53 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!
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
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Chain 1: Adjust your expectations accordingly!
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Saving _problems/test-vcov-100.R
Saving _problems/test-vcov-130.R
Saving _problems/test-vcov-169.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] -10.829 0.0
gamma[5] -22.986 0.0
gamma[6] 0.620 1.9
gamma[7] -28.931 0.0
gamma[8] -74.954 0.0
gamma[9] -66.824 0.0
gamma[10] -118.873 0.0
gamma[11] -56.147 0.0
gamma[12] 0.054 1.1
Loglik(model)= -744 Loglik(baseline only)= -744
n= 137, number of events= 128Saving _problems/test-8.R
[ FAIL 36 | WARN 12 | SKIP 1 | PASS 593 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• Set SPSURV_RUN_COVERAGE=true to run coverage check (1): 'test-coverage.R:6:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-coverage-gaps.R:26:3'): parsnip PO and AFT engines support survival, time, and linear_pred ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─testthat::expect_true(...) at test-coverage-gaps.R:26:3
2. │ └─testthat::quasi_label(enquo(object), label)
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. ├─".pred" %in% ...
5. ├─stats::predict(po_fit, veteran[1:2, ], type = "survival", eval_time = 100)
6. ├─parsnip::predict.model_fit(...)
7. │ ├─parsnip::predict_survival(...)
8. │ └─parsnip::predict_survival.model_fit(...)
9. │ └─rlang::eval_tidy(pred_call)
10. └─spsurv::spsurv_pred_survival(...)
11. ├─stats::predict(fit, newdata = new_data, type = "survival", eval_time = eval_time)
12. └─spsurv:::predict.spbp(...)
13. └─spsurv:::.spbp_predict_survival_censored(object, newdata, eval_time)
14. └─spsurv:::.spbp_survival_at_times(object, newdata, eval_time)
15. ├─survival::survfit(...)
16. └─spsurv:::survfit.spbp(...)
17. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
18. └─survival:::survfit.coxph(...)
19. └─survival:::model.matrix.coxph(object, data = mf)
20. └─stats::delete.response(object$terms)
21. └─stats (local) off(a$specials[[i]])
── Error ('test-coverage-gaps.R:62:3'): spsurv_pred helpers work on wrapped parsnip fits ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. └─spsurv::spsurv_pred_survival(...) at test-coverage-gaps.R:62:3
2. ├─stats::predict(fit, newdata = new_data, type = "survival", eval_time = eval_time)
3. └─spsurv:::predict.spbp(...)
4. └─spsurv:::.spbp_predict_survival_censored(object, newdata, eval_time)
5. └─spsurv:::.spbp_survival_at_times(object, newdata, eval_time)
6. ├─survival::survfit(...)
7. └─spsurv:::survfit.spbp(...)
8. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
9. └─survival:::survfit.coxph(...)
10. └─survival:::model.matrix.coxph(object, data = mf)
11. └─stats::delete.response(object$terms)
12. └─stats (local) off(a$specials[[i]])
── Error ('test-coverage-gaps.R:174:3'): augment supports eval_time and row-name matching ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─generics::augment(...) at test-coverage-gaps.R:174:3
2. └─spsurv:::augment.spbp(...)
3. └─spsurv:::.spbp_predict_survival_censored(x, newdata = data, eval_time = eval_time)
4. └─spsurv:::.spbp_survival_at_times(object, newdata, eval_time)
5. ├─survival::survfit(...)
6. └─spsurv:::survfit.spbp(...)
7. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
8. └─survival:::survfit.coxph(...)
9. └─survival:::model.matrix.coxph(object, data = mf)
10. └─stats::delete.response(object$terms)
11. └─stats (local) off(a$specials[[i]])
── Error ('test-coverage-gaps.R:184:3'): predict covers curve conf types and newdata curves ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(fit, times = c(0, 50, 100), type = "log-log") at test-coverage-gaps.R:184:3
2. └─spsurv:::predict.spbp(fit, times = c(0, 50, 100), type = "log-log")
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, ...)
6. └─survival:::survfit.coxph(formula = coxfit, ...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
── Error ('test-coverage-gaps.R:303:3'): survfit as.data.frame and tidy newdata merge ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, times = seq(0, 200, length.out = 20)) at test-coverage-gaps.R:303:3
2. └─spsurv:::survfit.spbp(fit, times = seq(0, 200, length.out = 20))
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-coverage-gaps.R:427:3'): predict survival uses training data when newdata is NULL ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(...) at test-coverage-gaps.R:427:3
2. └─spsurv:::predict.spbp(...)
3. └─spsurv:::.spbp_predict_survival_censored(object, newdata, eval_time)
4. └─spsurv:::.spbp_survival_at_times(object, newdata, eval_time)
5. ├─survival::survfit(...)
6. └─spsurv:::survfit.spbp(...)
7. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
8. └─survival:::survfit.coxph(...)
9. └─survival:::model.matrix.coxph(object, data = mf)
10. └─stats::delete.response(object$terms)
11. └─stats (local) off(a$specials[[i]])
── Error ('test-model-comparison.R:214:3'): model-comparison workflow runs end-to-end ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(fits$full, times = c(0, 100, 200)) at test-model-comparison.R:214:3
2. └─spsurv:::predict.spbp(fits$full, times = c(0, 100, 200))
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, ...)
6. └─survival:::survfit.coxph(formula = coxfit, ...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
── Error ('test-parsnip.R:21:3'): parsnip MLE fit and censored predict ─────────
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(...) at test-parsnip.R:21:3
2. ├─parsnip::predict.model_fit(...)
3. │ ├─parsnip::predict_survival(...)
4. │ └─parsnip::predict_survival.model_fit(...)
5. │ └─rlang::eval_tidy(pred_call)
6. └─spsurv::spsurv_pred_survival(...)
7. ├─stats::predict(fit, newdata = new_data, type = "survival", eval_time = eval_time)
8. └─spsurv:::predict.spbp(...)
9. └─spsurv:::.spbp_predict_survival_censored(object, newdata, eval_time)
10. └─spsurv:::.spbp_survival_at_times(object, newdata, eval_time)
11. ├─survival::survfit(...)
12. └─spsurv:::survfit.spbp(...)
13. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
14. └─survival:::survfit.coxph(...)
15. └─survival:::model.matrix.coxph(object, data = mf)
16. └─stats::delete.response(object$terms)
17. └─stats (local) off(a$specials[[i]])
── Error ('test-parsnip.R:44:3'): workflow with spsurv engine ──────────────────
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(fit, veteran[1:2, ], type = "survival", eval_time = 100) at test-parsnip.R:44:3
2. ├─workflows:::predict.workflow(...)
3. │ ├─stats::predict(fit, new_data, type = type, opts = opts, ...)
4. │ └─parsnip::predict.model_fit(...)
5. │ ├─parsnip::predict_survival(...)
6. │ └─parsnip::predict_survival.model_fit(...)
7. │ └─rlang::eval_tidy(pred_call)
8. └─spsurv::spsurv_pred_survival(...)
9. ├─stats::predict(fit, newdata = new_data, type = "survival", eval_time = eval_time)
10. └─spsurv:::predict.spbp(...)
11. └─spsurv:::.spbp_predict_survival_censored(object, newdata, eval_time)
12. └─spsurv:::.spbp_survival_at_times(object, newdata, eval_time)
13. ├─survival::survfit(...)
14. └─spsurv:::survfit.spbp(...)
15. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
16. └─survival:::survfit.coxph(...)
17. └─survival:::model.matrix.coxph(object, data = mf)
18. └─stats::delete.response(object$terms)
19. └─stats (local) off(a$specials[[i]])
── Error ('test-predict-censored.R:5:3'): predict.spbp survival returns nested censored format ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(...) at test-predict-censored.R:5:3
2. └─spsurv:::predict.spbp(...)
3. └─spsurv:::.spbp_predict_survival_censored(object, newdata, eval_time)
4. └─spsurv:::.spbp_survival_at_times(object, newdata, eval_time)
5. ├─survival::survfit(...)
6. └─spsurv:::survfit.spbp(...)
7. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
8. └─survival:::survfit.coxph(...)
9. └─survival:::model.matrix.coxph(object, data = mf)
10. └─stats::delete.response(object$terms)
11. └─stats (local) off(a$specials[[i]])
── Error ('test-predict-censored.R:13:3'): predict.spbp time returns median survival ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(fit, veteran[1:3, ], type = "time") at test-predict-censored.R:13:3
2. └─spsurv:::predict.spbp(fit, veteran[1:3, ], type = "time")
3. └─spsurv:::.spbp_predict_time_censored(object, newdata)
4. ├─base::data.frame(...)
5. └─spsurv:::.spbp_predict_median_time(object, newdata)
6. ├─survival::survfit(object, newdata = newdata, times = times, tidy = TRUE)
7. └─spsurv:::survfit.spbp(...)
8. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
9. └─survival:::survfit.coxph(...)
10. └─survival:::model.matrix.coxph(object, data = mf)
11. └─stats::delete.response(object$terms)
12. └─stats (local) off(a$specials[[i]])
── Error ('test-predict-censored.R:27:3'): predict.spbp curve mode backward compatible ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(fit, times = c(0, 10, 20)) at test-predict-censored.R:27:3
2. └─spsurv:::predict.spbp(fit, times = c(0, 10, 20))
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, ...)
6. └─survival:::survfit.coxph(formula = coxfit, ...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit-variance-gradients.R:130:3'): BPAFT production survfit cumhaz matches endpoint-corrected map and FD gradients ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, newdata = nd, times = t0) at test-survfit-variance-gradients.R:130:3
2. └─spsurv:::survfit.spbp(fit, newdata = nd, times = t0)
3. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
4. └─survival:::survfit.coxph(...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit-variance-gradients.R:190:3'): PO survfit std.err follows delta method when gamma information is stable ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, times = t0) at test-survfit-variance-gradients.R:190:3
2. └─spsurv:::survfit.spbp(fit, times = t0)
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit-variance-gradients.R:230:3'): non-finite gamma information does not crash diagnostics or summary/survfit ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─testthat::expect_warning(sf <- survfit(bad, times = 50), "non-finite|Bernstein") at test-survfit-variance-gradients.R:230:3
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(bad, times = 50)
7. └─spsurv:::survfit.spbp(bad, times = 50)
8. ├─survival::survfit(formula = coxfit, ...)
9. └─survival:::survfit.coxph(formula = coxfit, ...)
10. └─survival:::model.matrix.coxph(object, data = mf)
11. └─stats::delete.response(object$terms)
12. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit-variance-gradients.R:238:3'): survfit point estimates satisfy survival identity ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, times = seq(0, 120, by = 10)) at test-survfit-variance-gradients.R:238:3
2. └─spsurv:::survfit.spbp(fit, times = seq(0, 120, by = 10))
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:9:3'): survfit.spbp returns survfit object for MLE ───
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit) at test-survfit.R:9:3
2. └─spsurv:::survfit.spbp(fit)
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:29:3'): bayesian survfit default grid yields monotone credible bands ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, type = "plain", interval.type = "hpd") at test-survfit.R:29:3
2. └─spsurv:::survfit.spbp(fit, type = "plain", interval.type = "hpd")
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:41:3'): survfit.spbp with newdata ────────────────────
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, newdata = nd) at test-survfit.R:41:3
2. └─spsurv:::survfit.spbp(fit, newdata = nd)
3. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
4. └─survival:::survfit.coxph(...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:51:5'): survfit.spbp resolves training data via call stack when data slot is absent ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. └─base::eval(...)
2. └─base::eval(...)
3. ├─survival::survfit(fit) at test-survfit.R:51:5
4. └─spsurv:::survfit.spbp(fit)
5. ├─survival::survfit(formula = coxfit, ...)
6. └─survival:::survfit.coxph(formula = coxfit, ...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:58:3'): survfit.spbp for null model ──────────────────
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit) at test-survfit.R:58:3
2. └─spsurv:::survfit.spbp(fit)
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Failure ('test-survfit.R:65:3'): survfit.spbp errors when times is neither Surv nor numeric ──
`survfit(fit, times = letters[1:3])` threw an error with unexpected message.
Expected match: "'times' must be numeric or a Surv object"
Actual message: "subscript out of bounds"
Backtrace:
▆
1. ├─testthat::expect_error(survfit(fit, times = letters[1:3]), "'times' must be numeric or a Surv object") at test-survfit.R:65:3
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 = letters[1:3])
7. └─spsurv:::survfit.spbp(fit, times = letters[1:3])
8. ├─survival::survfit(formula = coxfit, ...)
9. └─survival:::survfit.coxph(formula = coxfit, ...)
10. └─survival:::model.matrix.coxph(object, data = mf)
11. └─stats::delete.response(object$terms)
12. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:71:3'): survfit.spbp accepts numeric times for smooth evaluation grid ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, times = g) at test-survfit.R:71:3
2. └─spsurv:::survfit.spbp(fit, times = g)
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:78:3'): plot.survfitbp draws lines not steps ─────────
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, times = seq(0, 200, length.out = 40)) at test-survfit.R:78:3
2. └─spsurv:::survfit.spbp(fit, times = seq(0, 200, length.out = 40))
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:91:3'): as.data.frame.survfitbp stacks curves for ggplot ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, newdata = nd, times = c(0, 10, 20, 50)) at test-survfit.R:91:3
2. └─spsurv:::survfit.spbp(...)
3. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
4. └─survival:::survfit.coxph(...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:99:3'): predict.spbp returns tidy survival data frame ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(fit, times = seq(0, 100, length.out = 25)) at test-survfit.R:99:3
2. └─spsurv:::predict.spbp(fit, times = seq(0, 100, length.out = 25))
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, ...)
6. └─survival:::survfit.coxph(formula = coxfit, ...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:107:3'): bpaft predict returns surv = 1 at time = 0 ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(fit, times = c(0, 50, 100)) at test-survfit.R:107:3
2. └─spsurv:::predict.spbp(fit, times = c(0, 50, 100))
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, ...)
6. └─survival:::survfit.coxph(formula = coxfit, ...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:115:3'): survfit.spbp summary reports training events on prediction grid ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, newdata = nd, times = seq(0, 200, by = 10)) at test-survfit.R:115:3
2. └─spsurv:::survfit.spbp(...)
3. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
4. └─survival:::survfit.coxph(...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:123:3'): survfit.spbp with times as Surv object updates time and n.event (lines 46-51) ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, times = times_surv) at test-survfit.R:123:3
2. └─spsurv:::survfit.spbp(fit, times = times_surv)
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:161:3'): survfit.spbp tidy = TRUE returns data.frame ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, times = c(0, 10, 20, 50), tidy = TRUE) at test-survfit.R:161:3
2. └─spsurv:::survfit.spbp(fit, times = c(0, 10, 20, 50), tidy = TRUE)
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:170:3'): survfit tidy with newdata includes covariate columns ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, newdata = nd, times = g, tidy = TRUE) at test-survfit.R:170:3
2. └─spsurv:::survfit.spbp(fit, newdata = nd, times = g, tidy = TRUE)
3. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
4. └─survival:::survfit.coxph(...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-survfit.R:178:3'): predict matches survfit tidy for same times ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─stats::predict(fit, times = g) at test-survfit.R:178:3
2. └─spsurv:::predict.spbp(fit, times = g)
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, ...)
6. └─survival:::survfit.coxph(formula = coxfit, ...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
── Error ('test-vcov.R:100:3'): survfit std.err matches delta method with vcov for PH ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(fit, times = t0) at test-vcov.R:100:3
2. └─spsurv:::survfit.spbp(fit, times = t0)
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
── Error ('test-vcov.R:127:3'): ill-conditioned gamma block still returns survfit uncertainty with warning ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
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. ├─survival::survfit(formula = coxfit, ...)
9. └─survival:::survfit.coxph(formula = coxfit, ...)
10. └─survival:::model.matrix.coxph(object, data = mf)
11. └─stats::delete.response(object$terms)
12. └─stats (local) off(a$specials[[i]])
── Error ('test-vcov.R:169:3'): ill-conditioned gamma warning appears only for survfit, not after fit ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─testthat::expect_warning(survfit(fit, times = 50), "lower Bernstein degree") at test-vcov.R:169:3
2. │ └─testthat:::quasi_capture(...)
3. │ ├─testthat (local) .capture(...)
4. │ │ └─base::withCallingHandlers(...)
5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
6. ├─survival::survfit(fit, times = 50)
7. └─spsurv:::survfit.spbp(fit, times = 50)
8. ├─survival::survfit(formula = coxfit, ...)
9. └─survival:::survfit.coxph(formula = coxfit, ...)
10. └─survival:::model.matrix.coxph(object, data = mf)
11. └─stats::delete.response(object$terms)
12. └─stats (local) off(a$specials[[i]])
── Error ('test.R:8:1'): (code run outside of `test_that()`) ───────────────────
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─survival::survfit(ph_fit) at test.R:8:1
2. └─spsurv:::survfit.spbp(ph_fit)
3. ├─survival::survfit(formula = coxfit, ...)
4. └─survival:::survfit.coxph(formula = coxfit, ...)
5. └─survival:::model.matrix.coxph(object, data = mf)
6. └─stats::delete.response(object$terms)
7. └─stats (local) off(a$specials[[i]])
[ FAIL 36 | WARN 12 | SKIP 1 | PASS 593 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.1.0
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘bayesian-analysis.Rmd’ using rmarkdown
Quitting from bayesian-analysis.Rmd:192-200 [predict-bayes]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `a$specials[[i]]`:
! subscript out of bounds
---
Backtrace:
▆
1. ├─stats::predict(fit, times = seq(0, max(larynx$time), length.out = 121))
2. └─spsurv:::predict.spbp(fit, times = seq(0, max(larynx$time), length.out = 121))
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, ...)
6. └─survival:::survfit.coxph(formula = coxfit, ...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'bayesian-analysis.Rmd' failed with diagnostics:
subscript out of bounds
--- failed re-building ‘bayesian-analysis.Rmd’
--- re-building ‘bp-degree.Rmd’ using rmarkdown
[WARNING] Deprecated: --mathjax. Use --math-method=mathjax[:URL] instead.
--- finished re-building ‘bp-degree.Rmd’
--- re-building ‘diagnostics.Rmd’ using rmarkdown
[WARNING] Deprecated: --mathjax. Use --math-method=mathjax[:URL] instead.
--- finished re-building ‘diagnostics.Rmd’
--- re-building ‘getting-started.Rmd’ using rmarkdown
Quitting from getting-started.Rmd:145-169 [km-bp-overlay]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `a$specials[[i]]`:
! subscript out of bounds
---
Backtrace:
▆
1. ├─stats::predict(fit, newdata = newdata, times = plot_times)
2. └─spsurv:::predict.spbp(fit, newdata = newdata, times = plot_times)
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
6. └─survival:::survfit.coxph(...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'getting-started.Rmd' failed with diagnostics:
subscript out of bounds
--- failed re-building ‘getting-started.Rmd’
--- re-building ‘mle-inference.Rmd’ using rmarkdown
[WARNING] Deprecated: --mathjax. Use --math-method=mathjax[:URL] instead.
--- finished re-building ‘mle-inference.Rmd’
--- re-building ‘model-families.Rmd’ using rmarkdown
Quitting from model-families.Rmd:110-133 [survival-overlay]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `a$specials[[i]]`:
! subscript out of bounds
---
Backtrace:
▆
1. ├─stats::predict(fit_ph, newdata = newdata, times = plot_times)
2. └─spsurv:::predict.spbp(fit_ph, newdata = newdata, times = plot_times)
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
6. └─survival:::survfit.coxph(...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'model-families.Rmd' failed with diagnostics:
subscript out of bounds
--- failed re-building ‘model-families.Rmd’
--- re-building ‘survival-ggplot.Rmd’ using rmarkdown
Quitting from survival-ggplot.Rmd:98-101 [predict]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `a$specials[[i]]`:
! subscript out of bounds
---
Backtrace:
▆
1. ├─stats::predict(fit, newdata = newdata, times = plot_times)
2. └─spsurv:::predict.spbp(fit, newdata = newdata, times = plot_times)
3. ├─survival::survfit(...)
4. └─spsurv:::survfit.spbp(...)
5. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
6. └─survival:::survfit.coxph(...)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'survival-ggplot.Rmd' failed with diagnostics:
subscript out of bounds
--- failed re-building ‘survival-ggplot.Rmd’
--- re-building ‘tidymodels.Rmd’ using rmarkdown
Quitting from tidymodels.Rmd:45-51 [parsnip-fit]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `a$specials[[i]]`:
! subscript out of bounds
---
Backtrace:
▆
1. ├─stats::predict(...)
2. ├─parsnip::predict.model_fit(...)
3. │ ├─parsnip::predict_survival(...)
4. │ └─parsnip::predict_survival.model_fit(...)
5. │ └─rlang::eval_tidy(pred_call)
6. └─spsurv::spsurv_pred_survival(...)
7. ├─stats::predict(fit, newdata = new_data, type = "survival", eval_time = eval_time)
8. └─spsurv:::predict.spbp(...)
9. └─spsurv:::.spbp_predict_survival_censored(object, newdata, eval_time)
10. └─spsurv:::.spbp_survival_at_times(object, newdata, eval_time)
11. ├─survival::survfit(...)
12. └─spsurv:::survfit.spbp(...)
13. ├─survival::survfit(formula = coxfit, newdata = newdata, ...)
14. └─survival:::survfit.coxph(...)
15. └─survival:::model.matrix.coxph(object, data = mf)
16. └─stats::delete.response(object$terms)
17. └─stats (local) off(a$specials[[i]])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'tidymodels.Rmd' failed with diagnostics:
subscript out of bounds
--- failed re-building ‘tidymodels.Rmd’
SUMMARY: processing the following files failed:
‘bayesian-analysis.Rmd’ ‘getting-started.Rmd’ ‘model-families.Rmd’
‘survival-ggplot.Rmd’ ‘tidymodels.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [33s/58s]
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 6.5e-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: 0.018 seconds (Warm-up)
Chain 1: 0.015 seconds (Sampling)
Chain 1: 0.033 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: 0.008 seconds (Warm-up)
Chain 1: 0.013 seconds (Sampling)
Chain 1: 0.021 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000324 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.24 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: 0.006 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 4.3e-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: 0.008 seconds (Warm-up)
Chain 1: 0.001 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: 0.008 seconds (Warm-up)
Chain 1: 0.012 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: 0.029 seconds (Warm-up)
Chain 1: 0.007 seconds (Sampling)
Chain 1: 0.036 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-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)
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Chain 1:
Chain 1: Elapsed Time: 0.035 seconds (Warm-up)
Chain 1: 0.022 seconds (Sampling)
Chain 1: 0.057 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: 0.005 seconds (Warm-up)
Chain 1: 0 seconds (Sampling)
Chain 1: 0.005 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)
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Chain 1:
Chain 1: Elapsed Time: 0.021 seconds (Warm-up)
Chain 1: 0.024 seconds (Sampling)
Chain 1: 0.045 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)
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Chain 1: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.063 seconds (Warm-up)
Chain 1: 0.035 seconds (Sampling)
Chain 1: 0.098 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: 0.028 seconds (Warm-up)
Chain 1: 0.038 seconds (Sampling)
Chain 1: 0.066 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: 0.01 seconds (Warm-up)
Chain 1: 0.026 seconds (Sampling)
Chain 1: 0.036 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000345 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.45 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: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.16 seconds (Warm-up)
Chain 1: 0.268 seconds (Sampling)
Chain 1: 0.428 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: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.015 seconds (Warm-up)
Chain 1: 0.006 seconds (Sampling)
Chain 1: 0.021 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-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: 0.021 seconds (Warm-up)
Chain 1: 0.022 seconds (Sampling)
Chain 1: 0.043 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: 0.02 seconds (Warm-up)
Chain 1: 0.02 seconds (Sampling)
Chain 1: 0.04 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.7e-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: 0.016 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.017 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: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.023 seconds (Warm-up)
Chain 1: 0.037 seconds (Sampling)
Chain 1: 0.06 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: Iteration: 10 / 10 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.016 seconds (Warm-up)
Chain 1: 0.017 seconds (Sampling)
Chain 1: 0.033 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000329 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.29 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: 0.013 seconds (Warm-up)
Chain 1: 0.019 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: 0.028 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.029 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.8e-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: 0.011 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.012 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: 0.005 seconds (Warm-up)
Chain 1: 0 seconds (Sampling)
Chain 1: 0.005 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: 0.02 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.021 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-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: 0.02 seconds (Warm-up)
Chain 1: 0.019 seconds (Sampling)
Chain 1: 0.039 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-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)
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Chain 1:
Chain 1: Elapsed Time: 0.019 seconds (Warm-up)
Chain 1: 0.02 seconds (Sampling)
Chain 1: 0.039 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.4e-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)
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Chain 1:
Chain 1: Elapsed Time: 0.02 seconds (Warm-up)
Chain 1: 0.037 seconds (Sampling)
Chain 1: 0.057 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: 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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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).
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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 4.9e-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: 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: 0.001 seconds (Warm-up)
Chain 1: 0.023 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 0.000111 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.11 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)
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: 0.047 seconds (Warm-up)
Chain 1: 0.018 seconds (Sampling)
Chain 1: 0.065 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 0.000346 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.46 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: 0.031 seconds (Warm-up)
Chain 1: 0.046 seconds (Sampling)
Chain 1: 0.077 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)
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 47 | 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 47 | 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’ [32s/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 6.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.63 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: 0.001 seconds (Sampling)
Chain 1: 0.008 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: 0.005 seconds (Warm-up)
Chain 1: 0.015 seconds (Sampling)
Chain 1: 0.02 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000317 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.17 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)
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Chain 1:
Chain 1: Elapsed Time: 0.451 seconds (Warm-up)
Chain 1: 0.457 seconds (Sampling)
Chain 1: 0.908 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: 0.007 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.008 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.6e-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: 0.007 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 4.7e-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 seconds (Sampling)
Chain 1: 0.019 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.8e-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: 0.037 seconds (Warm-up)
Chain 1: 0.025 seconds (Sampling)
Chain 1: 0.062 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: 0.008 seconds (Warm-up)
Chain 1: 0.02 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: 0.029 seconds (Warm-up)
Chain 1: 0.034 seconds (Sampling)
Chain 1: 0.063 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 5.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.51 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: 0.015 seconds (Sampling)
Chain 1: 0.031 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: 0.016 seconds (Warm-up)
Chain 1: 0.011 seconds (Sampling)
Chain 1: 0.027 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.6e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.66 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: 0.018 seconds (Sampling)
Chain 1: 0.024 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000338 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.38 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: 0.078 seconds (Sampling)
Chain 1: 0.102 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: 0.004 seconds (Warm-up)
Chain 1: 0 seconds (Sampling)
Chain 1: 0.004 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.7e-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: 0.011 seconds (Warm-up)
Chain 1: 0.012 seconds (Sampling)
Chain 1: 0.023 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:
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Chain 1:
Chain 1: Elapsed Time: 0.017 seconds (Warm-up)
Chain 1: 0.005 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:
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Chain 1:
Chain 1: Elapsed Time: 0.011 seconds (Warm-up)
Chain 1: 0.003 seconds (Sampling)
Chain 1: 0.014 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: 0.016 seconds (Warm-up)
Chain 1: 0.001 seconds (Sampling)
Chain 1: 0.017 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:
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Chain 1:
Chain 1: Elapsed Time: 0.008 seconds (Warm-up)
Chain 1: 0.004 seconds (Sampling)
Chain 1: 0.012 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:
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Chain 1:
Chain 1: Elapsed Time: 0.219 seconds (Warm-up)
Chain 1: 0.041 seconds (Sampling)
Chain 1: 0.26 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: 0.008 seconds (Warm-up)
Chain 1: 0.004 seconds (Sampling)
Chain 1: 0.012 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: 0.013 seconds (Warm-up)
Chain 1: 0.002 seconds (Sampling)
Chain 1: 0.015 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: 0.007 seconds (Warm-up)
Chain 1: 0.002 seconds (Sampling)
Chain 1: 0.009 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.5e-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: 0.016 seconds (Sampling)
Chain 1: 0.033 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 6.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.63 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: 0.009 seconds (Warm-up)
Chain 1: 0.002 seconds (Sampling)
Chain 1: 0.011 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 4.7e-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
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1).
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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 4.6e-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 2).
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Chain 2: Gradient evaluation took 4e-05 seconds
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SAMPLING FOR MODEL 'spbp' NOW (CHAIN 3).
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Chain 3: Gradient evaluation took 4.5e-05 seconds
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Chain 4:
Chain 4: Elapsed Time: 0.01 seconds (Warm-up)
Chain 4: 0.021 seconds (Sampling)
Chain 4: 0.031 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 4.9e-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: 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: 0.001 seconds (Warm-up)
Chain 1: 0.022 seconds (Sampling)
Chain 1: 0.023 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 6.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.64 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: 0.021 seconds (Warm-up)
Chain 1: 0.011 seconds (Sampling)
Chain 1: 0.032 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 0.000325 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.25 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: 0.037 seconds (Sampling)
Chain 1: 0.042 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 4.8e-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)
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 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-oldrel-macos-x86_64