Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929580762202112 |
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| author | McGillycuddy, Maeve Popovic, Gordana Bolker, Benjamin M. Warton, David I. |
| author_facet | McGillycuddy, Maeve Popovic, Gordana Bolker, Benjamin M. Warton, David I. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04411 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB McGillycuddy, Maeve Popovic, Gordana Bolker, Benjamin M. Warton, David I. Methodology 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. |
| title | Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB |
| topic | Methodology |
| url | https://arxiv.org/abs/2411.04411 |