Fast fitting of phylogenetic mixed effects models

Abstract

Mixed effects models are among the most commonly used statistical methods for the exploration of multispecies data. In recent years, also Joint Species Distribution Models and Generalized Linear Latent Variale Models have gained in popularity when the goal is to incorporate residual covariation between species that cannot be explained due to measured environmental covariates. Few software implementations of such models exist that can additionally incorporate phylogenetic information, and those that exist tend to utilize Markov chain Monte Carlo methods for estimation, so that model fitting takes a long time. In this article we develop new methods for quickly and flexibly fitting phylogenetic mixed models, potentially incorporating residual covariation between species using latent variables, with the possibility to estimate the strength of phylogenetic structuring in species responses per environmental covariate, and while incorporating correlation between different covariate effects. By combining Variational approximations with a reduced rank matrix normal covariance structure, Nearest Neighbours Gaussian Processes, and parallel computation, phylogenetic mixed models can be fitted much more quickly than the current state-of-the-art. Two simulation studies demonstrate that the proposed combination of approximations is not only fast, but also enjoys high accuracy. Finally, we demonstrate use of the method with a real world dataset of wood-decaying fungi.