Model Diagnostics Based on Cumulative Residuals: The R-package gof

TL;DR

This paper introduces an R package for model diagnostics in generalized linear models and structural equation models, using cumulative residuals and simulation-based methods to assess model fit and assumptions.

Contribution

It provides a new implementation of residual-based diagnostics in R, including tools for checking the proportional hazard assumption in Cox regression.

Findings

Effective residual diagnostics for GLMs and SEMs.

Simulation-based approach improves model assumption testing.

Includes tools for Cox proportional hazards model diagnostics.

Abstract

The generalized linear model is widely used in all areas of applied statistics and while correct asymptotic inference can be achieved under misspecification of the distributional assumptions, a correctly specified mean structure is crucial to obtain interpretable results. Usually the linearity and functional form of predictors are checked by inspecting various scatter plots of the residuals, however, the subjective task of judging these can be challenging. In this paper we present an implementation of model diagnostics for the generalized linear model as well as structural equation models, based on aggregates of the residuals where the asymptotic behavior under the null is imitated by simulations. A procedure for checking the proportional hazard assumption in the Cox regression is also implemented.