Table of contents
Project description
In many ecological studies, counts or biomass of interacting species are collected from several sites. Such data are often very sparse, high-dimensional and include highly correlated responses, and the main aim of the statistical analysis is to understand relationships among such multiple, correlated responses. Recent studies have shown that generalized linear latent variable models can be easily used to analyse data common in ecological studies. By extending the standard generalized linear modelling framework to include latent variables, we can account for any covariation between species not accounted for by the predictors, species interactions and correlations driven by missing covariates. As usual, a model-based approach gives us tools for diagnostics, model selection and statistical inference. Computationally efficient algorithms for fitting generalized linear latent variable models are proposed.
