“Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB” (2025), by Maeve McGillycuddy et al. Mathematics research paper. Original abstract below, unchanged. A free PDF is available.

Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB

Maeve McGillycuddy, David I. Warton, Gordana C. Popovic et al. · January 1, 2025 · Mathematics

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.

Free PDF DOI: 10.18637/jss.v112.i01