Last updated on 2026-10-24 06:51:07 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 15.88 | 521.58 | 537.46 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 10.69 | 364.13 | 374.82 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 11.00 | 386.71 | 397.71 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 10.00 | 388.01 | 398.01 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 17.00 | 484.00 | 501.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 15.48 | 488.77 | 504.25 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 14.72 | 503.70 | 518.42 | OK | |
| r-release-macos-arm64 | 1.5.0 | 3.00 | 135.00 | 138.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 10.00 | 418.00 | 428.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 16.00 | 459.00 | 475.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 3.00 | 133.00 | 136.00 | OK | |
| r-oldrel-macos-x86_64 | 1.5.0 | 10.00 | 593.00 | 603.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 22.00 | 636.00 | 658.00 | OK |
Version: 1.5.0
Check: examples
Result: ERROR
Running examples in ‘bregr-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: br_predict
> ### Title: Predict method for breg objects
> ### Aliases: br_predict
>
> ### ** Examples
>
> # Cox regression example
> if (requireNamespace("survival", quietly = TRUE)) {
+ lung <- survival::lung |> dplyr::filter(ph.ecog != 3)
+ mds <- br_pipeline(
+ lung,
+ y = c("time", "status"),
+ x = c("age", "ph.ecog"),
+ x2 = "sex",
+ method = "coxph"
+ )
+ scores <- br_predict(mds)
+ head(scores)
+ }
exponentiate estimates of model(s) constructed from coxph method at default
`idx` not set, use the first model
`type` is not specified, use lp for the model
Error in `value[[3L]]()`:
! failed to generate predictions: subscript out of bounds
Backtrace:
▆
1. └─bregr::br_predict(mds)
2. └─base::tryCatch(...)
3. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
4. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
5. └─value[[3L]](cond)
6. └─cli::cli_abort("failed to generate predictions: {e$message}")
7. └─rlang::abort(...)
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
br_compare_models 5.005 0.045 6.189
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [191s/240s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
>
> library(testthat)
> library(bregr)
Welcome to 'bregr' package!
=======================================================================
You are using bregr version 1.5.0
Project home : https://github.com/WangLabCSU/bregr
Documentation: https://wanglabcsu.github.io/bregr/
Cite as : https://doi.org/10.1002/mdr2.70028
Wang, S., Peng, Y., Shu, C., Wang, C., Yang, Y., Zhao, Y., Cui, Y., Hu, D. and Zhou, J.-G. (2025),
bregr: An R Package for Streamlined Batch Processing and Visualization of Biomedical Regression Models. Med Research.
=======================================================================
>
> test_check("bregr")
Building univariate models...
exponentiate estimates of model(s) constructed from coxph method at default
Building multivariate model...
exponentiate estimates of model(s) constructed from coxph method at default
Building univariate models...
exponentiate estimates of model(s) constructed from coxph method at default
Building multivariate model...
exponentiate estimates of model(s) constructed from coxph method at default
Building univariate models...
exponentiate estimates of model(s) constructed from coxph method at default
Building multivariate model...
exponentiate estimates of model(s) constructed from coxph method at default
xlim will be deprecated, use set_xaxis() instead.
Building univariate models...
exponentiate estimates of model(s) constructed from coxph method at default
Building multivariate model...
exponentiate estimates of model(s) constructed from coxph method at default
filtered variables: "x2" and "x3"
filtered variables: "x2"
Pre-filtering removed 2 out of 2 focal variables (100%)
filtered variables: "x1" and "x2"
filtered variables: "x2"
filtered variables: "x2"
exponentiate estimates of model(s) constructed from coxph method at default
`idx` not set, use the first model
Cox model: intercept term present but no intercept coefficient (as expected for semi-parametric models)
`idx` not set, use the first model
Cox model: intercept term present but no intercept coefficient (as expected for semi-parametric models)
Cox model: intercept term present but no intercept coefficient (as expected for semi-parametric models)
`idx` not set, use the first model
`idx` not set, use the first model
`idx` not set, use the first model
exponentiate estimates of model(s) constructed from coxph method at default
exponentiate estimates of model(s) constructed from coxph method at default
`idx` not set, use the first model
Cox model: intercept term present but no intercept coefficient (as expected for semi-parametric models)
Cox model: intercept term present but no intercept coefficient (as expected for semi-parametric models)
`idx` not set, use the first model
exponentiate estimates of model(s) constructed from coxph method at default
`idx` not set, use the first model
`type` is not specified, use lp for the model
Saving _problems/test-predict-survival-24.R
exponentiate estimates of model(s) constructed from coxph method at default
`idx` not set, use the first model
Saving _problems/test-predict-survival-55.R
`idx` not set, use the first model
`type` is not specified, use response for the model
`idx` not set, use the first model
subset model list with idx: 1
model call: stats::glm(formula = mpg ~ cyl + vs, family = stats::gaussian, data = data)
model call: stats::glm(formula = mpg ~ cyl + vs, family = stats::gaussian, data = data)
exponentiate estimates of model(s) constructed from coxph method at default
Saving _problems/test-roxytest-testexamples-04-show-101.R
exponentiate estimates of model(s) constructed from coxph method at default
`idx` not set, use the first model
Cox model: intercept term present but no intercept coefficient (as expected for semi-parametric models)
`idx` not set, use the first model
exponentiate estimates of model(s) constructed from coxph method at default
Loading required namespace: ggnewscale
please note only continuous focal terms analyzed and visualized
exponentiate estimates of model(s) constructed from coxph method at default
-- Model Diagnostics Summary ---------------------------------------------------
-- Model: "ph.ecog" (coxph) --
Sample size: 227
Events: 164
AIC: 1464.46
Log-likelihood: -729.23
Concordance: 12544, 7117, 126, 28, 0, 0.637, and 0.025
Proportional Hazards: Test failed
-- Model: "ph.karno" (coxph) --
Sample size: 227
Events: 164
AIC: 1476.157
Log-likelihood: -735.078
Concordance: 12578, 7145, 65, 28, 0, 0.637, and 0.025
Proportional Hazards: Test failed
-- Model: "pat.karno" (coxph) --
Sample size: 225
Events: 162
AIC: 1449.175
Log-likelihood: -721.587
Concordance: 12343, 6957, 57, 26, 0, 0.639, and 0.025
Proportional Hazards: Test failed
-- Model: "meal.cal" (coxph) --
Sample size: 181
Events: 134
AIC: 1153.136
Log-likelihood: -573.568
Concordance: 7761, 5080, 7, 17, 0, 0.604, and 0.029
Proportional Hazards: Test failed
-- Model: "wt.loss" (coxph) --
Sample size: 214
Events: 152
AIC: 1352.112
Log-likelihood: -673.056
Concordance: 10531, 6672, 10, 22, 0, 0.612, and 0.027
Proportional Hazards: Test failed
exponentiate estimates of model(s) constructed from coxph method at default
Building univariate models...
exponentiate estimates of model(s) constructed from coxph method at default
Building multivariate model...
exponentiate estimates of model(s) constructed from coxph method at default
Building univariate models...
exponentiate estimates of model(s) constructed from coxph method at default
Building multivariate model...
exponentiate estimates of model(s) constructed from coxph method at default
screening 3 genes against "survival" indicator
screening survival (survival)...
pre-filtering: no variables were filtered out
exponentiate estimates of model(s) constructed from coxph method at default
2 significant genes found
A <breg_seed_screen> object
Genes screened: 3
Genes significant (p < 0.5): 2
Significant genes: "GENE1" and "GENE3"
Use `$results` for full screening results
Step A: Lasso regression (lambda.min) on 5 genes
retained 3 genes from Lasso
Step B: Multivariate regression on 3 Lasso genes
exponentiate estimates of model(s) constructed from coxph method at default
retained 0 genes from multivariate regression
Step C: Stepwise regression (direction = both)
retained 1 genes from stepwise regression
v 1 final genes from intersection of all stages
A <breg_seed_select> object
Lasso genes: 3
Multivariate genes: 0
Stepwise genes: 1
Final genes: "GENE4"
building final coxph model with 3 genes
exponentiate estimates of model(s) constructed from coxph method at default
A <breg_seed_model> object
Genes in model: 3
Samples: 100
Risk scale: "none"
Risk Score Formula:
Risk Score = 1.1391 * GENE1 + 0.9763 * GENE2 + 1.1916 * GENE3
Overview of risk scores:
Min. 1st Qu. Median Mean 3rd Qu. Max.
-4.6024 -1.1862 0.1263 0.1211 1.5176 6.2341
-- SEED Pipeline ---------------------------------------------------------------
-- Step 1: Preliminary Screening --
screening 5 genes against "survival" indicator
screening survival (survival)...
pre-filtering: no variables were filtered out
exponentiate estimates of model(s) constructed from coxph method at default
2 significant genes found
Step 2: Enrichment Analysis (skipped)
-- Step 3: Gene Selection --
Step A: Lasso regression (lambda.min) on 2 genes
retained 2 genes from Lasso
Step B: Multivariate regression on 2 Lasso genes
exponentiate estimates of model(s) constructed from coxph method at default
retained 2 genes from multivariate regression
Step C: Stepwise regression (direction = both)
retained 1 genes from stepwise regression
v 1 final genes from intersection of all stages
-- Step 4: Model Development --
building final coxph model with 1 genes
exponentiate estimates of model(s) constructed from coxph method at default
A <breg_seed> object --- SEED pipeline results
-- Pipeline Summary
* Step 1 (Screening): 5 genes -> 2 significant
* Step 3 (Selection): 1 final genes
* Step 4 (Model): 1 genes, 100 samples
Risk Score Formula:
Risk Score = 1.3392 * GENE4
screening 5 genes against "survival" indicator
screening survival (survival)...
pre-filtering: no variables were filtered out
exponentiate estimates of model(s) constructed from coxph method at default
0 significant genes found
auto-detected 3 numeric gene columns
screening 3 genes against "survival" indicator
screening survival (survival)...
pre-filtering: no variables were filtered out
exponentiate estimates of model(s) constructed from coxph method at default
0 significant genes found
screening 3 genes against "ordinal" indicator
screening ordinal (ordinal)...
0 significant genes found
building final coxph model with 3 genes
exponentiate estimates of model(s) constructed from coxph method at default
extracting final genes from <breg_seed_select> object
building final coxph model with 3 genes
exponentiate estimates of model(s) constructed from coxph method at default
Step A: Lasso regression (lambda.min) on 5 genes
retained 4 genes from Lasso
Step B: Multivariate regression on 4 Lasso genes
exponentiate estimates of model(s) constructed from coxph method at default
retained 0 genes from multivariate regression
Step C: Stepwise regression (direction = both)
retained 1 genes from stepwise regression
v 1 final genes from intersection of all stages
Step A: Lasso regression (lambda.min) on 5 genes
retained 4 genes from Lasso
Step B: Multivariate regression on 4 Lasso genes
exponentiate estimates of model(s) constructed from coxph method at default
retained 0 genes from multivariate regression
Step C: Stepwise regression (direction = both)
retained 1 genes from stepwise regression
v 1 final genes from intersection of all stages
Step A: Lasso regression (lambda.min) on 5 genes
retained 4 genes from Lasso
Step B: Multivariate regression on 4 Lasso genes
exponentiate estimates of model(s) constructed from coxph method at default
retained 0 genes from multivariate regression
Step C: Stepwise regression (direction = both)
retained 1 genes from stepwise regression
v 1 final genes from intersection of all stages
screening 3 genes against "survival" indicator
screening survival (survival)...
pre-filtering: no variables were filtered out
exponentiate estimates of model(s) constructed from coxph method at default
3 significant genes found
building final coxph model with 3 genes
exponentiate estimates of model(s) constructed from coxph method at default
-- SEED Pipeline ---------------------------------------------------------------
-- Step 1: Preliminary Screening --
screening 5 genes against "survival" indicator
screening survival (survival)...
pre-filtering: no variables were filtered out
exponentiate estimates of model(s) constructed from coxph method at default
1 significant genes found
Step 2: Enrichment Analysis (skipped)
Step 3: Gene Selection (skipped --- need >= 2 genes)
-- Step 4: Model Development --
building final coxph model with 1 genes
exponentiate estimates of model(s) constructed from coxph method at default
exponentiate estimates of model(s) constructed from coxph method at default
`idx` not set, use the first model
Saving _problems/test-survival-legend-order-33.R
[ FAIL 4 | WARN 67 | SKIP 6 | PASS 167 ]
══ Skipped tests (6) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-nomogram-interactions.R:2:3'
• empty test (5): 'test-roxytest-testexamples-02-pipeline.R:5:1',
'test-roxytest-testexamples-03-accessors.R:5:1',
'test-roxytest-testexamples-04-show.R:5:1',
'test-roxytest-testexamples-04-show.R:201:1',
'test-roxytest-testexamples-05-polar.R:5:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-predict-survival.R:24:3'): br_predict works for Cox regression ──
Error in `value[[3L]](cond)`: failed to generate predictions: subscript out of bounds
Backtrace:
▆
1. └─bregr::br_predict(breg_obj) at test-predict-survival.R:24:3
2. └─base::tryCatch(...)
3. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
4. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
5. └─value[[3L]](cond)
6. └─cli::cli_abort("failed to generate predictions: {e$message}")
7. └─rlang::abort(...)
── Error ('test-predict-survival.R:55:3'): br_show_survival_curves works for Cox regression ──
Error in `value[[3L]](cond)`: failed to generate predictions: subscript out of bounds
Backtrace:
▆
1. └─bregr::br_show_survival_curves(breg_obj, n_groups = 2) at test-predict-survival.R:55:3
2. └─bregr::br_predict(breg, idx = idx, type = "lp")
3. └─base::tryCatch(...)
4. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
5. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
6. └─value[[3L]](cond)
7. └─cli::cli_abort("failed to generate predictions: {e$message}")
8. └─rlang::abort(...)
── Error ('test-roxytest-testexamples-04-show.R:101:3'): Function br_show_coxph_diagnostics() @ L493 ──
Error in `value[[3L]](cond)`: Failed to create Schoenfeld residuals plot: subscript out of bounds
Backtrace:
▆
1. └─bregr::br_show_coxph_diagnostics(mds, idx = 1) at test-roxytest-testexamples-04-show.R:101:3
2. └─bregr:::.br_show_coxph_diagnostics(...)
3. └─base::tryCatch(...)
4. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
5. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
6. └─value[[3L]](cond)
7. └─cli::cli_abort("Failed to create Schoenfeld residuals plot: {e$message}")
8. └─rlang::abort(...)
── Error ('test-survival-legend-order.R:33:3'): br_show_survival_curves legend shows groups in correct order ──
Error in `value[[3L]](cond)`: failed to generate predictions: subscript out of bounds
Backtrace:
▆
1. └─bregr::br_show_survival_curves(breg_obj, n_groups = 3) at test-survival-legend-order.R:33:3
2. └─bregr::br_predict(breg, idx = idx, type = "lp")
3. └─base::tryCatch(...)
4. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
5. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
6. └─value[[3L]](cond)
7. └─cli::cli_abort("failed to generate predictions: {e$message}")
8. └─rlang::abort(...)
[ FAIL 4 | WARN 67 | SKIP 6 | PASS 167 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc