Stepwise Logistic Regression in R: A Complete Guide
Key points Stepwise logistic regression is a technique for building a logistic model that iteratively selects or deselects predictors based on their statistical significance. Stepwise logistic regression can minimize model complexity and enhance model performance by removing irrelevant or redundant variables; nevertheless, it has significant drawbacks and limitations, such as sensitivity, bias, and ignorance of interactions or nonlinear effects. Stepwise logistic regression can be performed in R using the stepAIC function from the MASS package, which allows choosing the direction of the stepwise procedure, either "both," "backward," or "forward." Stepwise logistic regression should be interpreted and evaluated using various criteria, such as AIC, deviance, coefficients, p-values, odds ratios, confidence intervals, accuracy, precision, recall, F1-score, ROC curve, AUC, cross-validation, bootstrap, or hold-out test set. Stepwise logistic regression should be use…
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