Last updated on 2026-07-09 00:52:41 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.9.10 | 28.62 | 640.74 | 669.36 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.9.10 | 20.74 | 417.75 | 438.49 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.9.10 | 42.00 | 534.01 | 576.01 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 0.9.10 | 35.00 | 506.07 | 541.07 | ERROR | |
| r-devel-windows-x86_64 | 0.9.10 | 34.00 | 620.00 | 654.00 | OK | |
| r-patched-linux-x86_64 | 0.9.10 | 32.37 | 605.24 | 637.61 | OK | |
| r-release-linux-x86_64 | 0.9.10 | 27.46 | 604.08 | 631.54 | OK | |
| r-release-macos-arm64 | 0.9.10 | 7.00 | 132.00 | 139.00 | OK | |
| r-release-macos-x86_64 | 0.9.10 | 20.00 | 672.00 | 692.00 | OK | |
| r-release-windows-x86_64 | 0.9.10 | 35.00 | 622.00 | 657.00 | OK | |
| r-oldrel-macos-arm64 | 0.9.10 | 7.00 | 132.00 | 139.00 | OK | |
| r-oldrel-macos-x86_64 | 0.9.10 | 21.00 | 399.00 | 420.00 | OK | |
| r-oldrel-windows-x86_64 | 0.9.10 | 47.00 | 826.00 | 873.00 | OK |
Version: 0.9.10
Check: tests
Result: ERROR
Running ‘testthat.R’ [202s/203s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> pkg_name <- "tern"
> library(testthat)
> test_check(pkg_name, reporter = ParallelProgressReporter$new())
Loading required package: tern
Loading required package: rtables
Loading required package: formatters
Attaching package: 'formatters'
The following object is masked from 'package:base':
%||%
Loading required package: magrittr
Attaching package: 'magrittr'
The following objects are masked from 'package:testthat':
equals, is_less_than, not
Attaching package: 'rtables'
The following object is masked from 'package:utils':
str
Registered S3 method overwritten by 'tern':
method from
tidy.glm broom
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
✔ | F W S OK | Context
⠋ [ FAIL 0 | WARN 0 | SKIP 0 | PASS 0 ] Starting up...
✔ | 7 7 | abnormal [1.2s]
✔ | 5 10 | abnormal_by_baseline
✔ | 3 4 | abnormal_by_marked [4.1s]
✔ | 2 3 | abnormal_by_worst_grade
✔ | 8 12 | abnormal_lab_worsen_by_baseline [1.2s]
✔ | 31 83 | analyze_variables [8.0s]
✔ | 7 19 | analyze_vars_in_cols [12.3s]
✔ | 2 7 | bland-altman
✔ | 16 | combination_function
✔ | 9 20 | compare_variables [1.0s]
✔ | 1 3 | control_logistic
✔ | 1 4 | control_step
✔ | 3 11 | control_survival
✔ | 8 8 | count_cumulative
✔ | 4 4 | count_missed_doses
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd A.1.1.1.1 -> { dcd A.1.1.1.1, dcd A.1.1.1.1[2], dcd A.1.1.1.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd A.1.1.1.2 -> { dcd A.1.1.1.2, dcd A.1.1.1.2[2], dcd A.1.1.1.2[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd B.1.1.1.1 -> { dcd B.1.1.1.1, dcd B.1.1.1.1[2], dcd B.1.1.1.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd B.2.1.2.1 -> { dcd B.2.1.2.1, dcd B.2.1.2.1[2], dcd B.2.1.2.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd B.2.2.3.1 -> { dcd B.2.2.3.1, dcd B.2.2.3.1[2], dcd B.2.2.3.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd C.1.1.1.3 -> { dcd C.1.1.1.3, dcd C.1.1.1.3[2], dcd C.1.1.1.3[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd C.2.1.2.1 -> { dcd C.2.1.2.1, dcd C.2.1.2.1[2], dcd C.2.1.2.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd D.1.1.1.1 -> { dcd D.1.1.1.1, dcd D.1.1.1.1[2], dcd D.1.1.1.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd D.1.1.4.2 -> { dcd D.1.1.4.2, dcd D.1.1.4.2[2], dcd D.1.1.4.2[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd D.2.1.5.3 -> { dcd D.2.1.5.3, dcd D.2.1.5.3[2], dcd D.2.1.5.3[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[LOW -> { LOW, LOW[2], LOW[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[MEDIUM -> { MEDIUM, MEDIUM[2], MEDIUM[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[HIGH -> { HIGH, HIGH[2], HIGH[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[LOW -> { LOW, LOW[2], LOW[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[MEDIUM -> { MEDIUM, MEDIUM[2], MEDIUM[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[HIGH -> { HIGH, HIGH[2], HIGH[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
✔ | 8 20 | count_occurrences [4.7s]
✔ | 17 27 | count_occurrences_by_grade [4.9s]
✔ | 4 4 | count_patients_events_in_cols
✔ | 8 9 | count_patients_with_event
✔ | 11 18 | count_patients_with_flags [2.5s]
✔ | 11 17 | count_values [1.3s]
skipping term strata(ecog.ps)
✔ | 5 15 | coxph
✔ | 24 50 | coxreg [1.3s]
✔ | 2 13 | decorate_grob [2.1s]
✔ | 5 5 | desctools_binom_diff
✔ | 5 13 | df_explicit_na
✔ | 3 7 | estimate_multinomial_rsp
✔ | 18 24 | estimate_proportion [1.2s]
✔ | 3 16 | fit_rsp_step
✔ | 3 12 | fit_survival_step
✔ | 21 35 | formatting_functions
✔ | 3 18 | g_forest [8.3s]
✔ | 9 18 | g_km [12.6s]
✔ | 8 15 | g_lineplot [6.4s]
✔ | 4 16 | g_step
✔ | 2 2 | g_waterfall
✔ | 5 1 | h_adsl_adlb_merge_using_worst_flag
✔ | 2 5 | h_biomarkers_subgroups
✔ | 1 2 | h_format_row
✔ | 5 5 | h_incidence_rate
✔ | 13 26 | h_km [1.1s]
✔ | 16 62 | h_logistic_regression
✔ | 7 7 | h_map_for_count_abnormal
✔ | 2 2 | h_pkparam_sort
✔ | 4 4 | h_response_biomarkers_subgroups
✔ | 14 15 | h_response_subgroups
✔ | 3 8 | h_stack_by_baskets
✔ | 8 62 | h_step
✔ | 3 6 | h_survival_biomarkers_subgroups
✔ | 16 20 | h_survival_duration_subgroups
✔ | 2 0 | imputation_rule
✔ | 8 12 | incidence_rate [1.1s]
✔ | 7 | individual_patient_plot
✖ | 2 6 20 | logistic_regression [4.5s]
────────────────────────────────────────────────────────────────────────────────
Failure ('test-logistic_regression.R:102:3'): fit_logistic works with a single stratification variable
Expected `result_formula` to equal `expected_formula`.
Differences:
actual vs expected
- `Surv(1 + 0 * Response, Response) ~ ARMCD + RACE + AGE + strata(STRATA1)`
+ `Surv(rep(1, 20L), Response) ~ ARMCD + RACE + AGE + strata(STRATA1)`
Failure ('test-logistic_regression.R:134:3'): fit_logistic works with two stratification variables
Expected `result_formula` to equal `expected_formula`.
Differences:
actual vs expected
- `Surv(1 + 0 * Response, Response) ~ ARMCD + RACE + AGE + strata(I(interaction(STRATA1, `
+ `Surv(rep(1, 20L), Response) ~ ARMCD + RACE + AGE + strata(I(interaction(STRATA1, `
` STRATA2)))`
────────────────────────────────────────────────────────────────────────────────
✔ | 7 23 | make_afun
✔ | 8 12 | odds_ratio
✔ | 15 36 | prop_diff
✔ | 7 33 | prune_occurrences [4.7s]
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[biomarker_label -> { biomarker_label, biomarker_label[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
✔ | 6 13 | response_biomarkers_subgroups [3.3s]
✔ | 14 19 | response_subgroups [11.0s]
✔ | 5 15 | rtables_access [4.1s]
✔ | 5 8 | score_occurrences [1.8s]
✔ | 15 19 | split_cols_by_groups [1.1s]
✔ | 9 43 | stat
✔ | 1 5 13 | summarize_ancova [2.5s]
────────────────────────────────────────────────────────────────────────────────
Warning ('test-summarize_ancova.R:210:3'): summarize_ancova works with irregular arm levels
There was 1 warning in `mutate()`.
i In argument: `ARMCD = `%>%`(...)`.
Caused by warning:
! `case_match()` was deprecated in dplyr 1.2.0.
i Please use `recode_values()` instead.
Backtrace:
▆
1. ├─adsl %>% ... at test-summarize_ancova.R:210:3
2. ├─dplyr::mutate(...)
3. └─dplyr:::mutate.data.frame(...)
4. └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
5. └─dplyr:::signal_warnings(warnings_state, error_call)
────────────────────────────────────────────────────────────────────────────────
✔ | 5 7 | summarize_change
✔ | 3 3 | summarize_colvars [1.4s]
✔ | 13 23 | summarize_coxreg [10.4s]
✔ | 17 27 | summarize_glm_count [2.5s]
✔ | 17 27 | summarize_num_patients [3.8s]
✔ | 9 9 | summarize_patients_exposure_in_cols [1.5s]
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[biomarker_label -> { biomarker_label, biomarker_label[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
✔ | 7 11 | survival_biomarkers_subgroups [5.3s]
✔ | 9 11 | survival_coxph_pairwise [1.4s]
✔ | 14 21 | survival_duration_subgroups [12.0s]
✔ | 7 9 | survival_time [1.2s]
✔ | 10 11 | survival_timepoint [1.4s]
✔ | 16 30 | test_proportion_diff [1.4s]
✔ | 59 84 | utils [1.5s]
✔ | 42 | utils_checkmate
✔ | 6 47 | utils_default_stats_formats_labels
✔ | 13 32 | utils_factor
✔ | 3 3 | utils_ggplot [1.2s]
✔ | 8 | utils_grid
✔ | 19 34 | utils_rtables [8.2s]
✔ | 6 13 | utils_split_fun [2.6s]
══ Results ═════════════════════════════════════════════════════════════════════
Duration: 183.5 s
── Skipped tests (709) ─────────────────────────────────────────────────────────
• On CRAN (680): 'test-abnormal.R:1:1', 'test-abnormal.R:27:1',
'test-abnormal.R:54:1', 'test-abnormal.R:83:1', 'test-abnormal.R:106:1',
'test-abnormal.R:137:1', 'test-abnormal.R:163:1',
'test-abnormal_by_baseline.R:1:1', 'test-abnormal_by_baseline.R:28:1',
'test-abnormal_by_baseline.R:59:1', 'test-abnormal_by_baseline.R:83:1',
'test-abnormal_by_baseline.R:97:1', 'test-abnormal_by_marked.R:28:1',
'test-abnormal_by_marked.R:83:1', 'test-abnormal_by_marked.R:192:1',
'test-abnormal_by_worst_grade.R:19:1', 'test-abnormal_by_worst_grade.R:46:1',
'test-abnormal_lab_worsen_by_baseline.R:12:1',
'test-abnormal_lab_worsen_by_baseline.R:67:1',
'test-abnormal_lab_worsen_by_baseline.R:85:1',
'test-abnormal_lab_worsen_by_baseline.R:105:1',
'test-abnormal_lab_worsen_by_baseline.R:125:1',
'test-abnormal_lab_worsen_by_baseline.R:145:1',
'test-abnormal_lab_worsen_by_baseline.R:165:1',
'test-abnormal_lab_worsen_by_baseline.R:187:1',
'test-analyze_variables.R:1:1', 'test-analyze_variables.R:10:1',
'test-analyze_variables.R:26:1', 'test-analyze_variables.R:34:1',
'test-analyze_variables.R:42:1', 'test-analyze_variables.R:57:1',
'test-analyze_variables.R:65:1', 'test-analyze_variables.R:75:1',
'test-analyze_variables.R:84:1', 'test-analyze_variables.R:107:1',
'test-analyze_variables.R:121:1', 'test-analyze_variables.R:130:1',
'test-analyze_variables.R:137:1', 'test-analyze_variables.R:146:1',
'test-analyze_variables.R:156:1', 'test-analyze_variables.R:221:1',
'test-analyze_variables.R:244:1', 'test-analyze_variables.R:282:1',
'test-analyze_variables.R:305:1', 'test-analyze_variables.R:316:1',
'test-analyze_variables.R:331:1', 'test-analyze_variables.R:342:1',
'test-analyze_variables.R:353:1', 'test-analyze_variables.R:373:1',
'test-analyze_variables.R:399:1', 'test-analyze_variables.R:445:1',
'test-analyze_variables.R:458:1', 'test-analyze_variables.R:478:1',
'test-analyze_variables.R:495:1', 'test-analyze_variables.R:513:1',
'test-analyze_variables.R:528:1', 'test-analyze_vars_in_cols.R:3:1',
'test-analyze_vars_in_cols.R:42:1', 'test-analyze_vars_in_cols.R:93:1',
'test-analyze_vars_in_cols.R:148:1', 'test-analyze_vars_in_cols.R:216:1',
'test-analyze_vars_in_cols.R:279:1', 'test-analyze_vars_in_cols.R:384:1',
'test-bland-altman.R:64:1', 'test-compare_variables.R:1:1',
'test-compare_variables.R:12:1', 'test-compare_variables.R:23:1',
'test-compare_variables.R:35:1', 'test-compare_variables.R:60:1',
'test-compare_variables.R:72:1', 'test-compare_variables.R:93:1',
'test-compare_variables.R:103:1', 'test-compare_variables.R:118:1',
'test-control_logistic.R:1:1', 'test-control_step.R:1:1',
'test-control_survival.R:1:1', 'test-control_survival.R:15:1',
'test-control_survival.R:30:1', 'test-count_cumulative.R:1:1',
'test-count_cumulative.R:15:1', 'test-count_cumulative.R:33:1',
'test-count_cumulative.R:47:1', 'test-count_cumulative.R:66:1',
'test-count_cumulative.R:85:1', 'test-count_cumulative.R:107:1',
'test-count_cumulative.R:132:1', 'test-count_missed_doses.R:1:1',
'test-count_missed_doses.R:8:1', 'test-count_missed_doses.R:20:1',
'test-count_missed_doses.R:40:1', 'test-count_occurrences.R:1:1',
'test-count_occurrences.R:57:1', 'test-count_occurrences.R:93:1',
'test-count_occurrences.R:113:1', 'test-count_occurrences.R:139:1',
'test-count_occurrences.R:165:1', 'test-count_occurrences.R:206:1',
'test-count_occurrences.R:234:1', 'test-count_occurrences_by_grade.R:15:1',
'test-count_occurrences_by_grade.R:29:1',
'test-count_occurrences_by_grade.R:43:1',
'test-count_occurrences_by_grade.R:60:1',
'test-count_occurrences_by_grade.R:70:1',
'test-count_occurrences_by_grade.R:121:1',
'test-count_occurrences_by_grade.R:138:1',
'test-count_occurrences_by_grade.R:150:1',
'test-count_occurrences_by_grade.R:166:1',
'test-count_occurrences_by_grade.R:190:1',
'test-count_occurrences_by_grade.R:206:1',
'test-count_occurrences_by_grade.R:246:1',
'test-count_occurrences_by_grade.R:286:1',
'test-count_occurrences_by_grade.R:313:1',
'test-count_occurrences_by_grade.R:412:1',
'test-count_occurrences_by_grade.R:446:1',
'test-count_occurrences_by_grade.R:461:1',
'test-count_patients_events_in_cols.R:11:1',
'test-count_patients_events_in_cols.R:26:1',
'test-count_patients_events_in_cols.R:42:1',
'test-count_patients_events_in_cols.R:58:1',
'test-count_patients_with_event.R:1:1',
'test-count_patients_with_event.R:19:1',
'test-count_patients_with_event.R:38:1',
'test-count_patients_with_event.R:59:1',
'test-count_patients_with_event.R:83:1',
'test-count_patients_with_event.R:115:1',
'test-count_patients_with_event.R:153:1',
'test-count_patients_with_event.R:175:1',
'test-count_patients_with_flags.R:1:1',
'test-count_patients_with_flags.R:19:1',
'test-count_patients_with_flags.R:38:1',
'test-count_patients_with_flags.R:103:1',
'test-count_patients_with_flags.R:128:1',
'test-count_patients_with_flags.R:156:1',
'test-count_patients_with_flags.R:198:1',
'test-count_patients_with_flags.R:246:1',
'test-count_patients_with_flags.R:285:1',
'test-count_patients_with_flags.R:362:1',
'test-count_patients_with_flags.R:404:1', 'test-count_values.R:1:1',
'test-count_values.R:17:1', 'test-count_values.R:33:1',
'test-count_values.R:42:1', 'test-count_values.R:50:1',
'test-count_values.R:58:1', 'test-count_values.R:67:1',
'test-count_values.R:84:1', 'test-count_values.R:94:1',
'test-count_values.R:104:1', 'test-count_values.R:121:1', 'test-coxph.R:1:1',
'test-coxph.R:13:1', 'test-coxph.R:20:1', 'test-coxph.R:47:1',
'test-coxph.R:89:1', 'test-coxreg.R:36:1', 'test-coxreg.R:47:1',
'test-coxreg.R:59:1', 'test-coxreg.R:70:1', 'test-coxreg.R:83:1',
'test-coxreg.R:115:1', 'test-coxreg.R:129:1', 'test-coxreg.R:139:1',
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'test-utils_rtables.R:162:1', 'test-utils_rtables.R:172:1',
'test-utils_rtables.R:192:1', 'test-utils_rtables.R:205:1',
'test-utils_rtables.R:224:1', 'test-utils_rtables.R:231:1',
'test-utils_rtables.R:238:1', 'test-utils_rtables.R:252:1',
'test-utils_split_fun.R:9:1', 'test-utils_split_fun.R:30:1',
'test-utils_split_fun.R:42:1', 'test-utils_split_fun.R:59:1',
'test-utils_split_fun.R:77:1', 'test-utils_split_fun.R:105:1'
• no_plot_snapshots is TRUE (29): 'test-bland-altman.R:103:3',
'test-decorate_grob.R:124:3', 'test-g_forest.R:23:3', 'test-g_forest.R:58:3',
'test-g_forest.R:98:3', 'test-g_km.R:17:3', 'test-g_km.R:30:3',
'test-g_km.R:42:3', 'test-g_km.R:64:3', 'test-g_km.R:79:3',
'test-g_km.R:116:3', 'test-g_km.R:130:3', 'test-g_km.R:149:3',
'test-g_km.R:164:3', 'test-g_lineplot.R:9:3', 'test-g_lineplot.R:32:3',
'test-g_lineplot.R:52:3', 'test-g_lineplot.R:70:3',
'test-g_lineplot.R:101:3', 'test-g_lineplot.R:114:3',
'test-g_lineplot.R:249:3', 'test-g_lineplot.R:273:3', 'test-g_step.R:18:3',
'test-g_step.R:28:3', 'test-g_waterfall.R:7:3', 'test-g_waterfall.R:29:3',
'test-utils_ggplot.R:19:3', 'test-utils_ggplot.R:53:3',
'test-utils_ggplot.R:59:3'
── Failed tests ────────────────────────────────────────────────────────────────
Failure ('test-logistic_regression.R:102:3'): fit_logistic works with a single stratification variable
Expected `result_formula` to equal `expected_formula`.
Differences:
actual vs expected
- `Surv(1 + 0 * Response, Response) ~ ARMCD + RACE + AGE + strata(STRATA1)`
+ `Surv(rep(1, 20L), Response) ~ ARMCD + RACE + AGE + strata(STRATA1)`
Failure ('test-logistic_regression.R:134:3'): fit_logistic works with two stratification variables
Expected `result_formula` to equal `expected_formula`.
Differences:
actual vs expected
- `Surv(1 + 0 * Response, Response) ~ ARMCD + RACE + AGE + strata(I(interaction(STRATA1, `
+ `Surv(rep(1, 20L), Response) ~ ARMCD + RACE + AGE + strata(I(interaction(STRATA1, `
` STRATA2)))`
[ FAIL 2 | WARN 1 | SKIP 709 | PASS 1515 ]
Deleting unused snapshots: 'g_forest/g_forest_custom_2.svg',
'g_forest/g_forest_custom_3.svg', 'g_forest/g_forest_or.svg',
'g_forest/g_forest_plot_only.svg', 'g_km/g_km_crop_ylim_failure.svg',
'g_km/g_km_custom_ylim.svg', 'g_km/g_km_table_only.svg',
'g_lineplot/g_lineplot_table_only.svg', 'g_lineplot/g_lineplot_xlim_ylim.svg',
'g_lineplot/g_lineplot_xticks.svg', 'utils_ggplot/df2gg_cw.svg',
'utils_ggplot/df2gg_fs.svg', 'utils_ggplot/rtable2gg_cw.svg',
'utils_ggplot/rtable2gg_fs.svg', and 'utils_ggplot/rtable2gg_lblpad.svg'
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.9.10
Check: tests
Result: ERROR
Running ‘testthat.R’ [191s/193s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> pkg_name <- "tern"
> library(testthat)
> test_check(pkg_name, reporter = ParallelProgressReporter$new())
Loading required package: tern
Loading required package: rtables
Loading required package: formatters
Attaching package: 'formatters'
The following object is masked from 'package:base':
%||%
Loading required package: magrittr
Attaching package: 'magrittr'
The following objects are masked from 'package:testthat':
equals, is_less_than, not
Attaching package: 'rtables'
The following object is masked from 'package:utils':
str
Registered S3 method overwritten by 'tern':
method from
tidy.glm broom
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
✔ | F W S OK | Context
⠋ [ FAIL 0 | WARN 0 | SKIP 0 | PASS 0 ] Starting up...
✔ | 7 7 | abnormal [1.1s]
✔ | 5 10 | abnormal_by_baseline
✔ | 3 4 | abnormal_by_marked [4.0s]
✔ | 2 3 | abnormal_by_worst_grade
✔ | 8 12 | abnormal_lab_worsen_by_baseline [1.2s]
✔ | 31 83 | analyze_variables [7.6s]
✔ | 7 19 | analyze_vars_in_cols [11.5s]
✔ | 2 7 | bland-altman
✔ | 16 | combination_function
✔ | 9 20 | compare_variables
✔ | 1 3 | control_logistic
✔ | 1 4 | control_step
✔ | 3 11 | control_survival
✔ | 8 8 | count_cumulative
✔ | 4 4 | count_missed_doses
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd A.1.1.1.1 -> { dcd A.1.1.1.1, dcd A.1.1.1.1[2], dcd A.1.1.1.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd A.1.1.1.2 -> { dcd A.1.1.1.2, dcd A.1.1.1.2[2], dcd A.1.1.1.2[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd B.1.1.1.1 -> { dcd B.1.1.1.1, dcd B.1.1.1.1[2], dcd B.1.1.1.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd B.2.1.2.1 -> { dcd B.2.1.2.1, dcd B.2.1.2.1[2], dcd B.2.1.2.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd B.2.2.3.1 -> { dcd B.2.2.3.1, dcd B.2.2.3.1[2], dcd B.2.2.3.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd C.1.1.1.3 -> { dcd C.1.1.1.3, dcd C.1.1.1.3[2], dcd C.1.1.1.3[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd C.2.1.2.1 -> { dcd C.2.1.2.1, dcd C.2.1.2.1[2], dcd C.2.1.2.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd D.1.1.1.1 -> { dcd D.1.1.1.1, dcd D.1.1.1.1[2], dcd D.1.1.1.1[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd D.1.1.4.2 -> { dcd D.1.1.4.2, dcd D.1.1.4.2[2], dcd D.1.1.4.2[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd D.2.1.5.3 -> { dcd D.2.1.5.3, dcd D.2.1.5.3[2], dcd D.2.1.5.3[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[LOW -> { LOW, LOW[2], LOW[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[MEDIUM -> { MEDIUM, MEDIUM[2], MEDIUM[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[HIGH -> { HIGH, HIGH[2], HIGH[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[LOW -> { LOW, LOW[2], LOW[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[MEDIUM -> { MEDIUM, MEDIUM[2], MEDIUM[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[HIGH -> { HIGH, HIGH[2], HIGH[3] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
✔ | 8 20 | count_occurrences [4.4s]
✔ | 17 27 | count_occurrences_by_grade [4.6s]
✔ | 4 4 | count_patients_events_in_cols
✔ | 8 9 | count_patients_with_event
✔ | 11 18 | count_patients_with_flags [2.3s]
✔ | 11 17 | count_values [1.3s]
skipping term strata(ecog.ps)
✔ | 5 15 | coxph
✔ | 24 50 | coxreg [1.2s]
✔ | 2 13 | decorate_grob [1.9s]
✔ | 5 5 | desctools_binom_diff
✔ | 5 13 | df_explicit_na
✔ | 3 7 | estimate_multinomial_rsp
✔ | 18 24 | estimate_proportion [1.1s]
✔ | 3 16 | fit_rsp_step
✔ | 3 12 | fit_survival_step
✔ | 21 35 | formatting_functions
✔ | 3 18 | g_forest [7.7s]
✔ | 9 18 | g_km [11.9s]
✔ | 8 15 | g_lineplot [6.2s]
✔ | 4 16 | g_step
✔ | 2 2 | g_waterfall
✔ | 5 1 | h_adsl_adlb_merge_using_worst_flag
✔ | 2 5 | h_biomarkers_subgroups
✔ | 1 2 | h_format_row
✔ | 5 5 | h_incidence_rate
✔ | 13 26 | h_km [1.1s]
✔ | 16 62 | h_logistic_regression
✔ | 7 7 | h_map_for_count_abnormal
✔ | 2 2 | h_pkparam_sort
✔ | 4 4 | h_response_biomarkers_subgroups
✔ | 14 15 | h_response_subgroups
✔ | 3 8 | h_stack_by_baskets
✔ | 8 62 | h_step
✔ | 3 6 | h_survival_biomarkers_subgroups
✔ | 16 20 | h_survival_duration_subgroups
✔ | 2 0 | imputation_rule
✔ | 8 12 | incidence_rate
✔ | 7 | individual_patient_plot
✖ | 2 6 20 | logistic_regression [4.2s]
────────────────────────────────────────────────────────────────────────────────
Failure ('test-logistic_regression.R:102:3'): fit_logistic works with a single stratification variable
Expected `result_formula` to equal `expected_formula`.
Differences:
actual vs expected
- `Surv(1 + 0 * Response, Response) ~ ARMCD + RACE + AGE + strata(STRATA1)`
+ `Surv(rep(1, 20L), Response) ~ ARMCD + RACE + AGE + strata(STRATA1)`
Failure ('test-logistic_regression.R:134:3'): fit_logistic works with two stratification variables
Expected `result_formula` to equal `expected_formula`.
Differences:
actual vs expected
- `Surv(1 + 0 * Response, Response) ~ ARMCD + RACE + AGE + strata(I(interaction(STRATA1, `
+ `Surv(rep(1, 20L), Response) ~ ARMCD + RACE + AGE + strata(I(interaction(STRATA1, `
` STRATA2)))`
────────────────────────────────────────────────────────────────────────────────
✔ | 7 23 | make_afun
✔ | 8 12 | odds_ratio
✔ | 15 36 | prop_diff
✔ | 7 33 | prune_occurrences [4.6s]
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[biomarker_label -> { biomarker_label, biomarker_label[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
✔ | 6 13 | response_biomarkers_subgroups [3.1s]
✔ | 14 19 | response_subgroups [10.8s]
✔ | 5 15 | rtables_access [4.0s]
✔ | 5 8 | score_occurrences [1.8s]
✔ | 15 19 | split_cols_by_groups [1.0s]
✔ | 9 43 | stat
✔ | 1 5 13 | summarize_ancova [2.4s]
────────────────────────────────────────────────────────────────────────────────
Warning ('test-summarize_ancova.R:210:3'): summarize_ancova works with irregular arm levels
There was 1 warning in `mutate()`.
i In argument: `ARMCD = `%>%`(...)`.
Caused by warning:
! `case_match()` was deprecated in dplyr 1.2.0.
i Please use `recode_values()` instead.
Backtrace:
▆
1. ├─adsl %>% ... at test-summarize_ancova.R:210:3
2. ├─dplyr::mutate(...)
3. └─dplyr:::mutate.data.frame(...)
4. └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
5. └─dplyr:::signal_warnings(warnings_state, error_call)
────────────────────────────────────────────────────────────────────────────────
✔ | 5 7 | summarize_change
✔ | 3 3 | summarize_colvars [1.3s]
✔ | 13 23 | summarize_coxreg [9.9s]
✔ | 17 27 | summarize_glm_count [2.3s]
✔ | 17 27 | summarize_num_patients [3.7s]
✔ | 9 9 | summarize_patients_exposure_in_cols [1.5s]
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[biomarker_label -> { biomarker_label, biomarker_label[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root -> { root, root[2] }]
To control table names use split_rows_by*(, parent_name =.) or analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
✔ | 7 11 | survival_biomarkers_subgroups [5.2s]
✔ | 9 11 | survival_coxph_pairwise [1.3s]
✔ | 14 21 | survival_duration_subgroups [11.5s]
✔ | 7 9 | survival_time [1.1s]
✔ | 10 11 | survival_timepoint [1.3s]
✔ | 16 30 | test_proportion_diff [1.3s]
✔ | 59 84 | utils [1.5s]
✔ | 42 | utils_checkmate
✔ | 6 47 | utils_default_stats_formats_labels
✔ | 13 32 | utils_factor
✔ | 3 3 | utils_ggplot [1.1s]
✔ | 8 | utils_grid
✔ | 19 34 | utils_rtables [7.8s]
✔ | 6 13 | utils_split_fun [2.4s]
══ Results ═════════════════════════════════════════════════════════════════════
Duration: 174.7 s
── Skipped tests (709) ─────────────────────────────────────────────────────────
• On CRAN (680): 'test-abnormal.R:1:1', 'test-abnormal.R:27:1',
'test-abnormal.R:54:1', 'test-abnormal.R:83:1', 'test-abnormal.R:106:1',
'test-abnormal.R:137:1', 'test-abnormal.R:163:1',
'test-abnormal_by_baseline.R:1:1', 'test-abnormal_by_baseline.R:28:1',
'test-abnormal_by_baseline.R:59:1', 'test-abnormal_by_baseline.R:83:1',
'test-abnormal_by_baseline.R:97:1', 'test-abnormal_by_marked.R:28:1',
'test-abnormal_by_marked.R:83:1', 'test-abnormal_by_marked.R:192:1',
'test-abnormal_by_worst_grade.R:19:1', 'test-abnormal_by_worst_grade.R:46:1',
'test-abnormal_lab_worsen_by_baseline.R:12:1',
'test-abnormal_lab_worsen_by_baseline.R:67:1',
'test-abnormal_lab_worsen_by_baseline.R:85:1',
'test-abnormal_lab_worsen_by_baseline.R:105:1',
'test-abnormal_lab_worsen_by_baseline.R:125:1',
'test-abnormal_lab_worsen_by_baseline.R:145:1',
'test-abnormal_lab_worsen_by_baseline.R:165:1',
'test-abnormal_lab_worsen_by_baseline.R:187:1',
'test-analyze_variables.R:1:1', 'test-analyze_variables.R:10:1',
'test-analyze_variables.R:26:1', 'test-analyze_variables.R:34:1',
'test-analyze_variables.R:42:1', 'test-analyze_variables.R:57:1',
'test-analyze_variables.R:65:1', 'test-analyze_variables.R:75:1',
'test-analyze_variables.R:84:1', 'test-analyze_variables.R:107:1',
'test-analyze_variables.R:121:1', 'test-analyze_variables.R:130:1',
'test-analyze_variables.R:137:1', 'test-analyze_variables.R:146:1',
'test-analyze_variables.R:156:1', 'test-analyze_variables.R:221:1',
'test-analyze_variables.R:244:1', 'test-analyze_variables.R:282:1',
'test-analyze_variables.R:305:1', 'test-analyze_variables.R:316:1',
'test-analyze_variables.R:331:1', 'test-analyze_variables.R:342:1',
'test-analyze_variables.R:353:1', 'test-analyze_variables.R:373:1',
'test-analyze_variables.R:399:1', 'test-analyze_variables.R:445:1',
'test-analyze_variables.R:458:1', 'test-analyze_variables.R:478:1',
'test-analyze_variables.R:495:1', 'test-analyze_variables.R:513:1',
'test-analyze_variables.R:528:1', 'test-analyze_vars_in_cols.R:3:1',
'test-analyze_vars_in_cols.R:42:1', 'test-analyze_vars_in_cols.R:93:1',
'test-analyze_vars_in_cols.R:148:1', 'test-analyze_vars_in_cols.R:216:1',
'test-analyze_vars_in_cols.R:279:1', 'test-analyze_vars_in_cols.R:384:1',
'test-bland-altman.R:64:1', 'test-compare_variables.R:1:1',
'test-compare_variables.R:12:1', 'test-compare_variables.R:23:1',
'test-compare_variables.R:35:1', 'test-compare_variables.R:60:1',
'test-compare_variables.R:72:1', 'test-compare_variables.R:93:1',
'test-compare_variables.R:103:1', 'test-compare_variables.R:118:1',
'test-control_logistic.R:1:1', 'test-control_step.R:1:1',
'test-control_survival.R:1:1', 'test-control_survival.R:15:1',
'test-control_survival.R:30:1', 'test-count_cumulative.R:1:1',
'test-count_cumulative.R:15:1', 'test-count_cumulative.R:33:1',
'test-count_cumulative.R:47:1', 'test-count_cumulative.R:66:1',
'test-count_cumulative.R:85:1', 'test-count_cumulative.R:107:1',
'test-count_cumulative.R:132:1', 'test-count_missed_doses.R:1:1',
'test-count_missed_doses.R:8:1', 'test-count_missed_doses.R:20:1',
'test-count_missed_doses.R:40:1', 'test-count_occurrences.R:1:1',
'test-count_occurrences.R:57:1', 'test-count_occurrences.R:93:1',
'test-count_occurrences.R:113:1', 'test-count_occurrences.R:139:1',
'test-count_occurrences.R:165:1', 'test-count_occurrences.R:206:1',
'test-count_occurrences.R:234:1', 'test-count_occurrences_by_grade.R:15:1',
'test-count_occurrences_by_grade.R:29:1',
'test-count_occurrences_by_grade.R:43:1',
'test-count_occurrences_by_grade.R:60:1',
'test-count_occurrences_by_grade.R:70:1',
'test-count_occurrences_by_grade.R:121:1',
'test-count_occurrences_by_grade.R:138:1',
'test-count_occurrences_by_grade.R:150:1',
'test-count_occurrences_by_grade.R:166:1',
'test-count_occurrences_by_grade.R:190:1',
'test-count_occurrences_by_grade.R:206:1',
'test-count_occurrences_by_grade.R:246:1',
'test-count_occurrences_by_grade.R:286:1',
'test-count_occurrences_by_grade.R:313:1',
'test-count_occurrences_by_grade.R:412:1',
'test-count_occurrences_by_grade.R:446:1',
'test-count_occurrences_by_grade.R:461:1',
'test-count_patients_events_in_cols.R:11:1',
'test-count_patients_events_in_cols.R:26:1',
'test-count_patients_events_in_cols.R:42:1',
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'test-utils.R:402:1', 'test-utils.R:414:1', 'test-utils.R:426:1',
'test-utils.R:440:1', 'test-utils.R:449:1', 'test-utils.R:461:1',
'test-utils.R:473:1', 'test-utils.R:496:1', 'test-utils.R:508:1',
'test-utils.R:520:1', 'test-utils.R:534:1', 'test-utils.R:543:1',
'test-utils.R:555:1', 'test-utils.R:567:1', 'test-utils.R:578:1',
'test-utils.R:587:1', 'test-utils.R:599:1', 'test-utils.R:611:1',
'test-utils.R:625:1', 'test-utils.R:634:1', 'test-utils.R:646:1',
'test-utils.R:658:1', 'test-utils.R:672:1', 'test-utils.R:681:1',
'test-utils.R:693:1', 'test-utils.R:705:1',
'test-utils_default_stats_formats_labels.R:1:1',
'test-utils_default_stats_formats_labels.R:110:1',
'test-utils_default_stats_formats_labels.R:204:1',
'test-utils_default_stats_formats_labels.R:222:1',
'test-utils_default_stats_formats_labels.R:236:1',
'test-utils_default_stats_formats_labels.R:246:1',
'test-utils_factor.R:17:1', 'test-utils_factor.R:28:1',
'test-utils_factor.R:47:1', 'test-utils_factor.R:56:1',
'test-utils_factor.R:69:1', 'test-utils_factor.R:82:1',
'test-utils_factor.R:89:1', 'test-utils_factor.R:110:1',
'test-utils_factor.R:133:1', 'test-utils_factor.R:143:1',
'test-utils_factor.R:154:1', 'test-utils_factor.R:162:1',
'test-utils_factor.R:170:1', 'test-utils_rtables.R:1:1',
'test-utils_rtables.R:39:1', 'test-utils_rtables.R:47:1',
'test-utils_rtables.R:55:1', 'test-utils_rtables.R:69:1',
'test-utils_rtables.R:83:1', 'test-utils_rtables.R:90:1',
'test-utils_rtables.R:97:1', 'test-utils_rtables.R:107:1',
'test-utils_rtables.R:122:1', 'test-utils_rtables.R:147:1',
'test-utils_rtables.R:162:1', 'test-utils_rtables.R:172:1',
'test-utils_rtables.R:192:1', 'test-utils_rtables.R:205:1',
'test-utils_rtables.R:224:1', 'test-utils_rtables.R:231:1',
'test-utils_rtables.R:238:1', 'test-utils_rtables.R:252:1',
'test-utils_split_fun.R:9:1', 'test-utils_split_fun.R:30:1',
'test-utils_split_fun.R:42:1', 'test-utils_split_fun.R:59:1',
'test-utils_split_fun.R:77:1', 'test-utils_split_fun.R:105:1'
• no_plot_snapshots is TRUE (29): 'test-bland-altman.R:103:3',
'test-decorate_grob.R:124:3', 'test-g_forest.R:23:3', 'test-g_forest.R:58:3',
'test-g_forest.R:98:3', 'test-g_km.R:17:3', 'test-g_km.R:30:3',
'test-g_km.R:42:3', 'test-g_km.R:64:3', 'test-g_km.R:79:3',
'test-g_km.R:116:3', 'test-g_km.R:130:3', 'test-g_km.R:149:3',
'test-g_km.R:164:3', 'test-g_lineplot.R:9:3', 'test-g_lineplot.R:32:3',
'test-g_lineplot.R:52:3', 'test-g_lineplot.R:70:3',
'test-g_lineplot.R:101:3', 'test-g_lineplot.R:114:3',
'test-g_lineplot.R:249:3', 'test-g_lineplot.R:273:3', 'test-g_step.R:18:3',
'test-g_step.R:28:3', 'test-g_waterfall.R:7:3', 'test-g_waterfall.R:29:3',
'test-utils_ggplot.R:19:3', 'test-utils_ggplot.R:53:3',
'test-utils_ggplot.R:59:3'
── Failed tests ────────────────────────────────────────────────────────────────
Failure ('test-logistic_regression.R:102:3'): fit_logistic works with a single stratification variable
Expected `result_formula` to equal `expected_formula`.
Differences:
actual vs expected
- `Surv(1 + 0 * Response, Response) ~ ARMCD + RACE + AGE + strata(STRATA1)`
+ `Surv(rep(1, 20L), Response) ~ ARMCD + RACE + AGE + strata(STRATA1)`
Failure ('test-logistic_regression.R:134:3'): fit_logistic works with two stratification variables
Expected `result_formula` to equal `expected_formula`.
Differences:
actual vs expected
- `Surv(1 + 0 * Response, Response) ~ ARMCD + RACE + AGE + strata(I(interaction(STRATA1, `
+ `Surv(rep(1, 20L), Response) ~ ARMCD + RACE + AGE + strata(I(interaction(STRATA1, `
` STRATA2)))`
[ FAIL 2 | WARN 1 | SKIP 709 | PASS 1515 ]
Deleting unused snapshots: 'g_forest/g_forest_custom_2.svg',
'g_forest/g_forest_custom_3.svg', 'g_forest/g_forest_or.svg',
'g_forest/g_forest_plot_only.svg', 'g_km/g_km_crop_ylim_failure.svg',
'g_km/g_km_custom_ylim.svg', 'g_km/g_km_table_only.svg',
'g_lineplot/g_lineplot_table_only.svg', 'g_lineplot/g_lineplot_xlim_ylim.svg',
'g_lineplot/g_lineplot_xticks.svg', 'utils_ggplot/df2gg_cw.svg',
'utils_ggplot/df2gg_fs.svg', 'utils_ggplot/rtable2gg_cw.svg',
'utils_ggplot/rtable2gg_fs.svg', and 'utils_ggplot/rtable2gg_lblpad.svg'
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc