Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB

Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB

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.

Maeve McGillycuddy, Gordana Popovic, Benjamin M. Bolker, David I. Warton

https://www.jstatsoft.org/article/view/v112i01

OJS/PKP

11 APRIL 2025

FAJAR BAGUS W

PDF

ENGLISH

TEXT