Title: Conditional Model Selection in Mixed-Effects Models with cAIC4,
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.