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. > library(testthat) > library(regport) > > test_check("regport") Loading required package: see ========== Parameter | Coefficient | SE | 95% CI | z | p ------------------------------------------------------------- x | 2.23 | 1.83 | [0.45, 11.18] | 0.98 | 0.329 Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed using a Wald z-distribution approximation. [coxph] model ==========Never call '$get_forest_data()' before, run with default options to get plotting data ========== X(s): factor(cyl), disp, hp, drat Y(s): mpg covars: vs, am, factor(gear) Not build yet, run $build() method [] model ==========Please run $build() before $plot_forest() parallel computation from parallel package is not supported in Windows, disable it. ========== X(s): factor(cyl), disp, hp, drat Y(s): mpg covars: vs, am, factor(gear) ---- Result: focal_term variable estimate SE CI CI_low 1: factor(cyl) (Intercept) 23.28425686 3.102848398 0.95 17.20278575 2: factor(cyl) factor(cyl)6 -5.34044537 1.887666208 0.95 -9.04020315 3: factor(cyl) factor(cyl)8 -8.50258933 3.046261922 0.95 -14.47315299 4: factor(cyl) vs 1.68270326 2.353962963 0.95 -2.93097937 5: factor(cyl) am 4.31056447 2.156460225 0.95 0.08398010 6: factor(cyl) factor(gear)4 -1.24673744 2.264460155 0.95 -5.68499779 7: factor(cyl) factor(gear)5 -2.08223718 2.637768301 0.95 -7.25216805 8: disp (Intercept) 24.65009765 3.360529174 0.95 18.06358150 9: disp disp -0.02821806 0.009243695 0.95 -0.04633537 10: disp vs 3.32060607 1.856416709 0.95 -0.31790382 11: disp am 4.67281942 2.094171633 0.95 0.56831844 12: disp factor(gear)4 -2.52785760 2.211888366 0.95 -6.86307914 13: disp factor(gear)5 -2.89344598 2.574494892 0.95 -7.93936325 14: hp (Intercept) 24.56544525 2.420389406 0.95 19.82156919 15: hp hp -0.05145024 0.012010845 0.95 -0.07499106 16: hp vs 3.01661776 1.574374865 0.95 -0.06910027 17: hp am 5.11093165 1.798753134 0.95 1.58544029 18: hp factor(gear)4 -1.34845141 1.896221483 0.95 -5.06497722 19: hp factor(gear)5 1.16396634 2.363608073 0.95 -3.46862036 20: drat (Intercept) 5.36259687 7.058661715 0.95 -8.47212587 21: drat drat 3.01056864 2.245957350 0.95 -1.39142687 22: drat vs 6.56480882 1.648702814 0.95 3.33341068 23: drat am 6.04958546 2.304973039 0.95 1.53192132 24: drat factor(gear)4 -2.50572707 2.796115713 0.95 -7.98601316 25: drat factor(gear)5 -3.13453090 3.068393855 0.95 -9.14847234 focal_term variable estimate SE CI CI_low CI_high t df_error p 1: 29.36572797 7.5041555 25 6.182588e-14 2: -1.64068758 -2.8291259 25 4.667533e-03 3: -2.53202568 -2.7911550 25 5.252031e-03 4: 6.29638589 0.7148385 25 4.747089e-01 5: 8.53714885 1.9989075 25 4.561837e-02 6: 3.19152291 -0.5505672 25 5.819304e-01 7: 3.08769369 -0.7893935 25 4.298820e-01 8: 31.23661380 7.3351834 26 2.214181e-13 9: -0.01010075 -3.0526817 26 2.268064e-03 10: 6.95911596 1.7887180 26 7.366023e-02 11: 8.77732040 2.2313450 26 2.565828e-02 12: 1.80736393 -1.1428504 26 2.531007e-01 13: 2.15247129 -1.1238888 26 2.610602e-01 14: 29.30932132 10.1493773 26 3.334845e-24 15: -0.02790942 -4.2836487 26 1.838531e-05 16: 6.10233579 1.9160734 26 5.535576e-02 17: 8.63642301 2.8413747 26 4.491950e-03 18: 2.36807440 -0.7111255 26 4.770065e-01 19: 5.79655303 0.4924532 26 6.223990e-01 20: 19.19731961 0.7597186 26 4.474228e-01 21: 7.41256416 1.3404389 26 1.801027e-01 22: 9.79620695 3.9818024 26 6.839463e-05 23: 10.56724960 2.6245797 26 8.675598e-03 24: 2.97455903 -0.8961457 26 3.701750e-01 25: 2.87941055 -1.0215543 26 3.069919e-01 CI_high t df_error p [glm/lm] model ==========[ FAIL 0 | WARN 5 | SKIP 0 | PASS 6 ] [ FAIL 0 | WARN 5 | SKIP 0 | PASS 6 ] > > proc.time() user system elapsed 4.07 0.53 4.62