Graphical Methods for Assessing Logistic Regression Models

Abstract In ordinary linear regression, graphical diagnostic displays can be very useful for detecting and examining anomalous features in the fit of a model to data. For logistic regression models, the discreteness of binary data makes it difficult to interpret such displays. Modifications and extensions of linear model displays lead to three methods for diagnostic checking of logistic regression models. Local mean deviance plots are useful for detecting overall lack of fit. Empirical probability plots help point out isolated departures from the fitted model. Partial residual plots, when smoothed to show underlying structure, help identify specific causes of lack of fit. These methods are illustrated through the analyses of simulated and real data.