Conditional Model Selection in Mixed-Effects Models with cAIC4

TL;DR

This paper introduces cAIC4, an R package for calculating the conditional AIC in mixed-effects models, enabling more accurate model selection by accounting for random effects uncertainty.

Contribution

The paper presents a new R package, cAIC4, providing fast, stable computation of conditional AIC and automated stepwise model selection for mixed models.

Findings

cAIC4 simplifies conditional model selection in mixed models.

The package is compatible with lme4 and gamm4.

Examples demonstrate practical applications and ease of use.

Abstract

Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this paper we introduce the R-package cAIC4 that allows for the computation of the conditional Akaike Information Criterion (cAIC). Computation of the conditional AIC needs to take into account the uncertainty of the random effects variance and is therefore not straightforward. We introduce a fast and stable implementation for the calculation of the cAIC for linear mixed models estimated with lme4 and additive mixed models estimated with gamm4 . Furthermore, cAIC4 offers a stepwise function that allows for a fully automated stepwise selection scheme for mixed models based on the conditional AIC. Examples of many possible applications are presented to illustrate the practical impact and easy handling of the package.