Residuals and Diagnostics for Binary and Ordinal Regression Models: An Introduction to the sure Package

Authors

• Brandon M. Greenwell, Wright State University
• Andrew J. McCarthy, The Perduco Group
• Bradley C. Boehmke, University of Cincinnati
• Dungang Liu, University of Cincinnati

Date of this Version

7-2018

Document Type

Article

Citation

The R Journal (July 2018) 10(1); Editor: John Verzani

Comments

Copyright 2018, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

Residual diagnostics is an important topic in the classroom, but it is less often used in practice when the response is binary or ordinal. Part of the reason for this is that generalized models for discrete data, like cumulative link models and logistic regression, do not produce standard residuals that are easily interpreted as those in ordinary linear regression. In this paper, we introduce the R package sure, which implements a recently developed idea of SUrrogate REsiduals. We demonstrate the utility of the package in detection of cumulative link model misspecification with respect to mean structures, link functions, heteroscedasticity, proportionality, and interaction effects