blorr
Tools for Developing Binary Logistic Regression Models
Tools designed to make it easier for beginner and intermediate users to build and validate binary logistic regression models. Includes bivariate analysis, comprehensive regression output, model fit statistics, variable selection procedures, model validation techniques and a 'shiny' app for interactive model building.
models
Overview
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.
Installation
# Install blorr from CRANinstall.packages("blorr")# Or the development version from GitHub# install.packages("devtools")devtools::install_github("rsquaredacademy/blorr")
Shiny App
Use blr_launch_app() to explore the package using a shiny app.
Articles
Usage
blorr uses consistent prefix blr_* for easy tab completion.
library(blorr)library(magrittr)
Bivariate Analysis
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 0s_count 1s_count 0s_dist 1s_dist 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#> -----------------------------
Model
# create model using glmmodel <- glm(honcomp ~ female + read + science, data = hsb2,family = binomial(link = 'logit'))
Regression Output
blr_regress(model)#> - Creating model overview.#> - Creating response profile.#> - Extracting maximum likelihood estimates.#> - Estimating concordant and discordant pairs.#> 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#> ---------------------------------------------------------------
Model Fit Statistics
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#> ---------------------------------------------------------------------------------
Confusion Matrix
blr_confusion_matrix(model)#> Confusion Matrix and Statistics#>#> Reference#> Prediction 0 1#> 0 135 26#> 1 12 27#>#> Accuracy : 0.81#> 95% CI : (0.7487, 0.8619)#> No Information Rate : 0.735#> P-Value [Acc > NIR] : 0.008453#>#> Kappa : 0.4673#> Mcnemar's Test P-Value : 0.034955#>#> 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#>#> 'Positive' Class : 1#>
Hosmer Lemeshow Test
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#> ------------------------------
Gains Table
blr_gains_table(model)#> Warning: Calling `as_tibble()` on a vector is discouraged, because the#> behavior is likely to change in the future. Use `enframe(name = NULL)`#> instead.#> # A tibble: 10 x 12#> decile total `1` `0` ks tp tn fp fn sensitivity#> <dbl> <int> <int> <int> <dbl> <int> <int> <int> <int> <dbl>#> 1 1 20 14 6 22.3 14 141 6 39 26.4#> 2 2 20 13 7 42.1 27 134 13 26 50.9#> 3 3 20 10 10 54.2 37 124 23 16 69.8#> 4 4 20 7 13 58.5 44 111 36 9 83.0#> 5 5 20 3 17 52.6 47 94 53 6 88.7#> 6 6 20 3 17 46.7 50 77 70 3 94.3#> 7 7 20 1 19 35.7 51 58 89 2 96.2#> 8 8 20 2 18 27.2 53 40 107 0 100#> 9 9 20 0 20 13.6 53 20 127 0 100#> 10 10 20 0 20 0 53 0 147 0 100#> specificity accuracy#> <dbl> <dbl>#> 1 95.9 77.5#> 2 91.2 80.5#> 3 84.4 80.5#> 4 75.5 77.5#> 5 63.9 70.5#> 6 52.4 63.5#> 7 39.5 54.5#> 8 27.2 46.5#> 9 13.6 36.5#> 10 0 26.5
Lift Chart
model %>%blr_gains_table() %>%plot()
ROC Curve
model %>%blr_gains_table() %>%blr_roc_curve()
KS Chart
model %>%blr_gains_table() %>%blr_ks_chart()
Lorenz Curve
blr_lorenz_curve(model)
Getting Help
If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.
Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
News
blorr 0.2.0
This is a minor release to fix bugs and for other enhancements.
Enhancements
- variable selection procedures now return the final model as an object of
class
glm(#58) - data preparation functions of selected plots are now exported to enable end users to create customized plots and to use plotting library of their choice (#64)
- informs users about new versions (#1)
Bug Fixes
- tibble package breaking changes (#74)
blorr 0.1.0
Initial release