Conditional Model Selection in Mixed-Effects Models with cAIC4

Conditional Model Selection in Mixed-Effects Models with cAIC4

conditional AIC, lme4, mixed-effects models, penalized splines.

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 (generalized) linear mixed models estimated with lme4 and (generalized)
additive mixed models estimated with gamm4. Furthermore, cAIC4 offers a stepwise
function that allows for an automated stepwise selection scheme for mixed models based on
the cAIC. Examples of many possible applications are presented to illustrate the practical
impact and easy handling of the package.

Benjamin Säfken

https://www.jstatsoft.org/article/view/v099i08

Georg-August Universität Göttingen

August 2021

Fajar Bagus W

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