R Under development (unstable) (2024-11-08 r87310 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 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(blorr) > > test_check("blorr") Backward Elimination Method --------------------------- Candidate Terms: 1 . female 2 . read 3 . science 4 . math 5 . prog 6 . socst We are eliminating variables based on p value... Variables Removed: - prog - socst No more variables satisfy the condition of p value = 0.3 Final Model Output ------------------ Model Overview ------------------------------------------------------------------------ Data Set Resp Var Obs. Df. Model Df. Residual Convergence ------------------------------------------------------------------------ data honcomp 200 199 195 TRUE ------------------------------------------------------------------------ Response Summary -------------------------------------------------------- Outcome Frequency Outcome Frequency -------------------------------------------------------- 0 147 1 53 -------------------------------------------------------- Maximum Likelihood Estimates ----------------------------------------------------------------- Parameter DF Estimate Std. Error z value Pr(>|z|) ----------------------------------------------------------------- (Intercept) 1 -14.5773 2.1568 -6.7589 0.0000 female1 1 1.3622 0.4605 2.9580 0.0031 read 1 0.0631 0.0281 2.2455 0.0247 science 1 0.0569 0.0326 1.7429 0.0814 math 1 0.1113 0.0338 3.2992 0.0010 ----------------------------------------------------------------- Association of Predicted Probabilities and Observed Responses --------------------------------------------------------------- % Concordant 0.8835 Somers' D 0.7669 % Discordant 0.1165 Gamma 0.7669 % Tied 0.0000 Tau-a 0.3003 Pairs 7791 c 0.8835 --------------------------------------------------------------- Variable: prog Weight of Evidence ------------------------------------------------------------------------- levels count_0s count_1s dist_0s dist_1s woe iv ------------------------------------------------------------------------- 1 38 7 0.26 0.13 0.67 0.08 2 65 40 0.44 0.75 -0.53 0.17 3 44 6 0.30 0.11 0.97 0.18 ------------------------------------------------------------------------- Information Value ----------------------------- Variable Information Value ----------------------------- prog 0.4329 ----------------------------- Variable: race Weight of Evidence ------------------------------------------------------------------------- levels count_0s count_1s dist_0s dist_1s woe iv ------------------------------------------------------------------------- 1 22 2 0.15 0.04 1.38 0.15 2 6 5 0.04 0.09 -0.84 0.04 3 18 2 0.12 0.04 1.18 0.10 4 101 44 0.69 0.83 -0.19 0.03 ------------------------------------------------------------------------- Information Value ----------------------------- Variable Information Value ----------------------------- race 0.326 ----------------------------- Variable: female Weight of Evidence ------------------------------------------------------------------------- levels count_0s count_1s dist_0s dist_1s woe iv ------------------------------------------------------------------------- 0 73 18 0.50 0.34 0.38 0.06 1 74 35 0.50 0.66 -0.27 0.04 ------------------------------------------------------------------------- Information Value ----------------------------- Variable Information Value ----------------------------- female 0.1023 ----------------------------- Variable: schtyp Weight of Evidence ------------------------------------------------------------------------ levels count_0s count_1s dist_0s dist_1s woe iv ------------------------------------------------------------------------ 1 123 45 0.84 0.85 -0.01 0.00 2 24 8 0.16 0.15 0.08 0.00 ------------------------------------------------------------------------ Information Value ----------------------------- Variable Information Value ----------------------------- schtyp 0.0012 ----------------------------- Hmmm.. Looks like you have specified an incorrect model. The below steps might be helpful: * Check if you have used the glm() function to build the model. * If you have never used it before, you can learn more by typing ?glm or help(glm) in the Console. Please specify the model in the below format: glm( write ~ 1 , data = hsb2 , family = binomial(link = 'logit')) Happy modeling :) Hmmm.. Looks like you have specified an incorrect model. The below steps might be helpful: * Check if you have used the glm() function to build the model. * If you have never used it before, you can learn more by typing ?glm or help(glm) in the Console. Please specify the model in the below format: glm( write ~ 1 , data = hsb2 , family = binomial(link = 'logit')) Happy modeling :) Forward Selection Method --------------------------- Candidate Terms: 1. female 2. read 3. science We are selecting variables based on p value... Variables Entered: - read - female - science Final Model Output ------------------ Model Overview ------------------------------------------------------------------------ Data Set Resp Var Obs. Df. Model Df. Residual Convergence ------------------------------------------------------------------------ data honcomp 200 199 196 TRUE ------------------------------------------------------------------------ Response Summary -------------------------------------------------------- Outcome Frequency Outcome Frequency -------------------------------------------------------- 0 147 1 53 -------------------------------------------------------- Maximum Likelihood Estimates ----------------------------------------------------------------- Parameter DF Estimate Std. Error z value Pr(>|z|) ----------------------------------------------------------------- (Intercept) 1 -12.7772 1.9755 -6.4677 0.0000 read 1 0.1035 0.0258 4.0186 1e-04 female1 1 1.4825 0.4474 3.3139 9e-04 science 1 0.0948 0.0305 3.1129 0.0019 ----------------------------------------------------------------- Association of Predicted Probabilities and Observed Responses --------------------------------------------------------------- % Concordant 0.8561 Somers' D 0.7147 % Discordant 0.1425 Gamma 0.7136 % Tied 0.0014 Tau-a 0.2794 Pairs 7791 c 0.8568 --------------------------------------------------------------- Hmmm.. Looks like you have specified an incorrect model. The below steps might be helpful: * Check if you have used the glm() function to build the model. * If you have never used it before, you can learn more by typing ?glm or help(glm) in the Console. Please specify the model in the below format: glm( honcomp ~ female + read + science , data = hsb2 , family = binomial(link = 'logit')) Happy modeling :)Hmmm.. Looks like you have specified an incorrect model. The below steps might be helpful: * Check if you have used the glm() function to build the model. * If you have never used it before, you can learn more by typing ?glm or help(glm) in the Console. Please specify the model in the below format: glm( read ~ female + science , data = hsb2 , family = binomial(link = 'logit')) Happy modeling :) cutoff can take on values between 0 and 1 only. You have used 1.4 , please specify a value between 0 and 1 only. cutoff can take on values between 0 and 1 only. You have used 1.4 , please specify a value between 0 and 1 only. Stepwise Selection Method ------------------------- Candidate Terms: 1 . x1 2 . x2 3 . x3 4 . x4 5 . x5 6 . x6 Variables Entered/Removed: - x6 added - x1 added - x3 added - x2 added - x6 removed - x5 added No more variables to be added or removed.Stepwise Selection Method --------------------------- Candidate Terms: 1. x1 2. x2 3. x3 4. x4 5. x5 6. x6 We are selecting variables based on p value... Variables Entered/Removed: - x6 added - x5 added - x6 added No more variables to be added/removed. Final Model Output ------------------ Model Overview ------------------------------------------------------------------------- Data Set Resp Var Obs. Df. Model Df. Residual Convergence ------------------------------------------------------------------------- data y 20000 19999 19995 TRUE ------------------------------------------------------------------------- Response Summary -------------------------------------------------------- Outcome Frequency Outcome Frequency -------------------------------------------------------- 0 10041 1 9959 -------------------------------------------------------- Maximum Likelihood Estimates ------------------------------------------------------------------ Parameter DF Estimate Std. Error z value Pr(>|z|) ------------------------------------------------------------------ (Intercept) 1 -1.5228 0.0287 -53.1302 0.0000 x1 1 1.0276 0.0212 48.4927 0.0000 x2 1 0.9807 0.0209 46.9690 0.0000 x3 1 1.0260 0.0213 48.2799 0.0000 x5 1 0.0357 0.0192 1.8531 0.0639 ------------------------------------------------------------------ Association of Predicted Probabilities and Observed Responses --------------------------------------------------------------- % Concordant 0.8911 Somers' D 0.7821 % Discordant 0.1089 Gamma 0.7821 % Tied 0.0000 Tau-a 0.3911 Pairs 99998319 c 0.8911 --------------------------------------------------------------- [ FAIL 0 | WARN 0 | SKIP 44 | PASS 56 ] ══ Skipped tests (44) ══════════════════════════════════════════════════════════ • On CRAN (44): 'test-backward-aic.R:8:3', 'test-bivariate-analysis.R:10:3', 'test-bivariate-analysis.R:24:3', 'test-bivariate-analysis.R:37:3', 'test-bivariate-analysis.R:50:3', 'test-bivariate-analysis.R:63:3', 'test-bivariate-analysis.R:69:3', 'test-blr-plots.R:7:3', 'test-blr-plots.R:15:3', 'test-blr-plots.R:23:3', 'test-blr-plots.R:31:3', 'test-blr-plots.R:39:3', 'test-blr-plots.R:47:3', 'test-blr-plots.R:55:3', 'test-blr-plots.R:63:3', 'test-blr-plots.R:71:3', 'test-blr-plots.R:79:3', 'test-blr-plots.R:88:3', 'test-blr-plots.R:96:3', 'test-blr-plots.R:104:3', 'test-blr-plots.R:112:3', 'test-blr-plots.R:120:3', 'test-blr-plots.R:128:3', 'test-blr-plots.R:137:3', 'test-blr-plots.R:145:3', 'test-blr-plots.R:153:3', 'test-blr-plots.R:161:3', 'test-blr-plots.R:169:3', 'test-blr-plots.R:177:3', 'test-blr-plots.R:185:3', 'test-blr-plots.R:194:3', 'test-blr-plots.R:202:3', 'test-blr-plots.R:210:3', 'test-blr-plots.R:218:3', 'test-blr-plots.R:228:3', 'test-blr-regress.R:9:3', 'test-coll-diag.R:33:3', 'test-forward-aic.R:8:3', 'test-hosmer-lemeshow.R:20:3', 'test-model-fit-stats.R:92:3', 'test-model-fit-stats.R:102:3', 'test-model-fit-stats.R:108:3', 'test-model-validation.R:9:3', 'test-stepwise-aic.R:4:3' [ FAIL 0 | WARN 0 | SKIP 44 | PASS 56 ] Deleting unused snapshots: • blr-plots/c-fitted-plot.svg • blr-plots/c-leverage-plot.svg • blr-plots/c-plot.svg • blr-plots/cbar-plot.svg • blr-plots/decile-capture-rate-chart.svg • blr-plots/decile-lift-chart.svg • blr-plots/deviance-fitted-plot.svg • blr-plots/deviance-residual-plot.svg • blr-plots/difchisq-fitted-plot.svg • blr-plots/difchisq-leverage-plot.svg • blr-plots/difchisq-plot.svg • blr-plots/difdev-fitted-plot.svg • blr-plots/difdev-leverage-plot.svg • blr-plots/difdev-plot.svg • blr-plots/fitted-leverage-plot.svg • blr-plots/forward-selection-plot.svg • blr-plots/ks-chart.svg • blr-plots/leverage-fitted-plot.svg • blr-plots/leverage-plot.svg • blr-plots/lift-chart.svg • blr-plots/lorenz-curve.svg • blr-plots/pearson-residual-plot.svg • blr-plots/residual-fitted-plot.svg • blr-plots/roc-curve.svg • blr-plots/segment-distribution-plot.svg • blr-plots/stepwise-backward-selection-plot.svg • blr-plots/stepwise-selection-plot.svg • blr-plots/woe-plot.svg • forward-p.md > > proc.time() user system elapsed 10.90 1.31 12.21