- Published
- 2025-01-01
- Venue
- Journal of Statistical Software, vol. 112, no. 1
- Cited by
- 495
- Open access
- Yes, diamond
- DOI
- 10.18637/jss.v112.i01
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.