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arXiv · 2411.04411

Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB

Maeve McGillycuddy·Gordana Popovic·Benjamin M. Bolker·David I. Warton
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Abstract

Multivariate random effects with unstructured variance-covariance matrices of large dimensions, $q$, can be a major challenge to estimate. In this paper, we introduce a new implementation of a reduced-rank approach to fit large dimensional multivariate random effects by writing them as a linear combination of $d < q$ latent variables. By adding reduced-rank functionality to the package glmmTMB, we enhance the mixed models available to include random effects of dimensions that were previously not possible. We apply the reduced-rank random effect to two examples, estimating a generalized latent variable model for multivariate abundance data and a random-slopes model.

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Maeve McGillycuddy, Gordana Popovic, Benjamin M. Bolker, David I. Warton. 2024-11-07. Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB. https://arxiv.org/abs/2411.04411

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