Confidence Intervals for Predicted Outcomes in Regression Models for Categorical Outcomes

Abstract

We discuss methods for computing confidence intervals for predictions and discrete changes in predictions for regression models for categorical outcomes. The methods include endpoint transformation, the delta method, and bootstrapping. We also describe an update to prvalue and prgen from the SPost package, which adds the ability to compute confidence intervals. The article provides several examples that illustrate the application of these methods.