varTestnlme: an R package for Variance Components Testing in Linear and Nonlinear Mixed-effects Models

TL;DR

The varTestnlme R package provides tools for testing variance components in linear, generalized linear, and nonlinear mixed-effects models, filling a gap in existing statistical testing tools.

Contribution

It introduces a new R package that enables variance components testing in mixed-effects models, including nonlinear models, using likelihood ratio tests.

Findings

The package performs well on real datasets.

It supports models fitted with lme4, nlme, or saemix.

Theoretical properties of the tests are validated.

Abstract

The issue of variance components testing arises naturally when building mixed-effects models, to decide which effects should be modeled as fixed or random. While tests for fixed effects are available in R for models fitted with lme4, tools are missing when it comes to random effects. The varTestnlme package for R aims at filling this gap. It allows to test whether any subset of the variances and covariances corresponding to any subset of the random effects, are equal to zero using asymptotic property of the likelihood ratio test statistic. It also offers the possibility to test simultaneously for fixed effects and variance components. It can be used for linear, generalized linear or nonlinear mixed-effects models fitted via lme4, nlme or saemix. Theoretical properties of the used likelihood ratio test are recalled, numerical methods used to implement the test procedure are detailed and examples based on different real datasets using different mixed models are provided.