# Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB

arXiv: https://arxiv.org/abs/2411.04411 · HTML: https://mlsanity.com/paper/2411.04411/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Generative AI Purpose-built for Social and Mental Health: A Real-World Pilot](https://mlsanity.com/paper/2511.11689/) (2025)

## Abstract (arXiv)

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.