Confidence intervals for predicted outcomes in regression models for categorical outcomes
Jun Xu
Indiana University
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J. Scott Long
Indiana University
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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.
View all articles by these authors:
Jun Xu, J. Scott Long
View all articles with these keywords:
prvalue, prgen, confidence interval, predicted probability, discrete choice models, endpoint transformation, delta method, bootstrap
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