# Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB

“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.

- Authors: Maeve McGillycuddy, David I. Warton, Gordana C. Popovic, Benjamin M. Bolker
- Published: January 1, 2025
- Field: Mathematics
- DOI: https://doi.org/10.18637/jss.v112.i01
- Free PDF: https://www.jstatsoft.org/index.php/jss/article/download/v112i01/4676

## 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.

Canonical page: https://nerdi.pages.dev/paper/W4409376239
Read three real papers a day in Nerdi (iPhone): https://apps.apple.com/app/apple-store/id6813826387?pt=128757794&ct=web-paper&mt=8