R Under development (unstable) (2025-08-18 r88641 ucrt) -- "Unsuffered Consequences" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # 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 1.2.0 Project home : https://github.com/WangLabCSU/bregr Documentation: https://wanglabcsu.github.io/bregr/ Cite as : arXiv:2110.14232 ======================================================================= > > test_check("bregr") 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 exponentiate estimates of model(s) constructed from coxph method at default `idx` not set, use the first model `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 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 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 Log-likelihood: -729.23 Concordance: 12544, 7117, 126, 28, 0, 0.637, and 0.025 Proportional Hazards Test (Schoenfeld Residuals): + ph.ecog: χ² = 2.054, df = 1, p = 0.152 + age: χ² = 0.188, df = 1, p = 0.665 + sex: χ² = 2.305, df = 1, p = 0.129 Global test: p = 0.216 - Assumption + SATISFIED -- Model: "ph.karno" (coxph) -- Sample size: 227 Events: 164 Log-likelihood: -735.078 Concordance: 12578, 7145, 65, 28, 0, 0.637, and 0.025 Proportional Hazards Test (Schoenfeld Residuals): x ph.karno: χ² = 8.017, df = 1, p = 0.00463 + age: χ² = 0.478, df = 1, p = 0.489 + sex: χ² = 3.085, df = 1, p = 0.079 Global test: p = 0.0157 - Assumption x VIOLATED -- Model: "pat.karno" (coxph) -- Sample size: 225 Events: 162 Log-likelihood: -721.587 Concordance: 12343, 6957, 57, 26, 0, 0.639, and 0.025 Proportional Hazards Test (Schoenfeld Residuals): x pat.karno: χ² = 4.226, df = 1, p = 0.0398 + age: χ² = 0.054, df = 1, p = 0.817 + sex: χ² = 2.752, df = 1, p = 0.0971 Global test: p = 0.0819 - Assumption + SATISFIED -- Model: "meal.cal" (coxph) -- Sample size: 181 Events: 134 Log-likelihood: -573.568 Concordance: 7761, 5080, 7, 17, 0, 0.604, and 0.029 Proportional Hazards Test (Schoenfeld Residuals): x meal.cal: χ² = 4.65, df = 1, p = 0.031 + age: χ² = 0.622, df = 1, p = 0.43 + sex: χ² = 1.481, df = 1, p = 0.224 Global test: p = 0.0942 - Assumption + SATISFIED -- Model: "wt.loss" (coxph) -- Sample size: 214 Events: 152 Log-likelihood: -673.056 Concordance: 10531, 6672, 10, 22, 0, 0.612, and 0.027 Proportional Hazards Test (Schoenfeld Residuals): + wt.loss: χ² = 0.014, df = 1, p = 0.904 + age: χ² = 0.508, df = 1, p = 0.476 + sex: χ² = 2.549, df = 1, p = 0.11 Global test: p = 0.391 - Assumption + SATISFIED exponentiate estimates of model(s) constructed from coxph method at default `idx` not set, use the first model `idx` not set, use the first model [ FAIL 0 | WARN 0 | SKIP 6 | PASS 113 ] ══ 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' [ FAIL 0 | WARN 0 | SKIP 6 | PASS 113 ] > > proc.time() user system elapsed 80.35 2.59 82.95