Tools for building binary logistic regression models
Tools designed to make it easier for users, particularly beginner/intermediate R users to build logistic regression models. Includes comprehensive regression output, variable selection procedures, model validation techniques and a ‘shiny’ app for interactive model building.
# Install blorr from CRAN
install.packages("blorr")
# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("rsquaredacademy/blorr")
# Install the development version from `rsquaredacademy` universe
install.packages("blorr", repos = "https://rsquaredacademy.r-universe.dev")
blorr uses consistent prefix blr_* for easy tab completion.
library(blorr) library(magrittr)
blr_bivariate_analysis(hsb2, honcomp, female, prog, race, schtyp) #> Bivariate Analysis #> --------------------------------------------------------------------- #> Variable Information Value LR Chi Square LR DF LR p-value #> --------------------------------------------------------------------- #> female 0.10 3.9350 1 0.0473 #> prog 0.43 16.1450 2 3e-04 #> race 0.33 11.3694 3 0.0099 #> schtyp 0.00 0.0445 1 0.8330 #> ---------------------------------------------------------------------Weight of Evidence & Information Value
blr_woe_iv(hsb2, prog, honcomp) #> 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 #> -----------------------------
# create model using glm
model <- glm(honcomp ~ female + read + science, data = hsb2,
family = binomial(link = 'logit'))
blr_regress(model) #> 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 #> female1 1 1.4825 0.4474 3.3139 9e-04 #> read 1 0.1035 0.0258 4.0186 1e-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 #> ---------------------------------------------------------------
blr_model_fit_stats(model) #> Model Fit Statistics #> --------------------------------------------------------------------------------- #> Log-Lik Intercept Only: -115.644 Log-Lik Full Model: -80.118 #> Deviance(196): 160.236 LR(3): 71.052 #> Prob > LR: 0.000 #> MCFadden's R2 0.307 McFadden's Adj R2: 0.273 #> ML (Cox-Snell) R2: 0.299 Cragg-Uhler(Nagelkerke) R2: 0.436 #> McKelvey & Zavoina's R2: 0.518 Efron's R2: 0.330 #> Count R2: 0.810 Adj Count R2: 0.283 #> BIC: 181.430 AIC: 168.236 #> ---------------------------------------------------------------------------------
blr_confusion_matrix(model) #> Confusion Matrix and Statistics #> #> Reference #> Prediction 0 1 #> 0 135 26 #> 1 12 27 #> #> #> Accuracy : 0.8100 #> No Information Rate : 0.7350 #> #> Kappa : 0.4673 #> #> McNemars's Test P-Value : 0.0350 #> #> Sensitivity : 0.5094 #> Specificity : 0.9184 #> Pos Pred Value : 0.6923 #> Neg Pred Value : 0.8385 #> Prevalence : 0.2650 #> Detection Rate : 0.1350 #> Detection Prevalence : 0.1950 #> Balanced Accuracy : 0.7139 #> Precision : 0.6923 #> Recall : 0.5094 #> #> 'Positive' Class : 1
blr_test_hosmer_lemeshow(model) #> Partition for the Hosmer & Lemeshow Test #> -------------------------------------------------------------- #> def = 1 def = 0 #> Group Total Observed Expected Observed Expected #> -------------------------------------------------------------- #> 1 20 0 0.16 20 19.84 #> 2 20 0 0.53 20 19.47 #> 3 20 2 0.99 18 19.01 #> 4 20 1 1.64 19 18.36 #> 5 21 3 2.72 18 18.28 #> 6 19 3 4.05 16 14.95 #> 7 20 7 6.50 13 13.50 #> 8 20 10 8.90 10 11.10 #> 9 20 13 11.49 7 8.51 #> 10 20 14 16.02 6 3.98 #> -------------------------------------------------------------- #> #> Goodness of Fit Test #> ------------------------------ #> Chi-Square DF Pr > ChiSq #> ------------------------------ #> 4.4998 8 0.8095 #> ------------------------------
blr_gains_table(model) #> decile total 1 0 ks tp tn fp fn sensitivity specificity accuracy #> 1 1 20 14 6 22.33346 14 141 6 39 26.41509 95.91837 77.5 #> 2 2 20 13 7 42.09986 27 134 13 26 50.94340 91.15646 80.5 #> 3 3 20 10 10 54.16506 37 124 23 16 69.81132 84.35374 80.5 #> 4 4 20 7 13 58.52907 44 111 36 9 83.01887 75.51020 77.5 #> 5 5 20 3 17 52.62482 47 94 53 6 88.67925 63.94558 70.5 #> 6 6 20 3 17 46.72058 50 77 70 3 94.33962 52.38095 63.5 #> 7 7 20 1 19 35.68220 51 58 89 2 96.22642 39.45578 54.5 #> 8 8 20 2 18 27.21088 53 40 107 0 100.00000 27.21088 46.5 #> 9 9 20 0 20 13.60544 53 20 127 0 100.00000 13.60544 36.5 #> 10 10 20 0 20 0.00000 53 0 147 0 100.00000 0.00000 26.5
model %>% blr_gains_table() %>% plot()
model %>% blr_gains_table() %>% blr_roc_curve()
model %>% blr_gains_table() %>% blr_ks_chart()
If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.
Please note that the blorr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
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