Landwehr, James M.; Pregibon, Daryl; Shoemaker, Anne C. Graphical methods for assessing logistic regression models. (English) Zbl 0531.65080 J. Am. Stat. Assoc. 79, 61-83 (1984). Summary: 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. Cited in 1 ReviewCited in 36 Documents MSC: 65C99 Probabilistic methods, stochastic differential equations 65S05 Graphical methods in numerical analysis 65D10 Numerical smoothing, curve fitting 62J05 Linear regression; mixed models Keywords:binary data; goodness of fit; residual analysis; near neighbors; probability plot; partial residual; graphical diagnostic displays; logistic regression Software:alr3 PDFBibTeX XMLCite \textit{J. M. Landwehr} et al., J. Am. Stat. Assoc. 79, 61--83 (1984; Zbl 0531.65080) Full Text: DOI