gllvm: Fast analysis of multivariate abundance data with generalized linear latent variable models in <scp>r</scp>

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  • Jenni Niku
    Department of Mathematics and Statistics University of Jyväskylä Jyväskylä Finland
  • Francis K. C. Hui
    Research School of Finance Actuarial Studies & Statistics Australian National University Canberra Australia
  • Sara Taskinen
    Department of Mathematics and Statistics University of Jyväskylä Jyväskylä Finland
  • David I. Warton
    School of Mathematics and Statistics and Evolution & Ecology Research Centre UNSW Sydney Canberra Australia
  • Sarah Goslee
    editor

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<jats:title>Abstract</jats:title><jats:p> <jats:list> <jats:list-item><jats:p>There has been rapid development in tools for multivariate analysis based on fully specified statistical models or ‘joint models’. One approach attracting a lot of attention is generalized linear latent variable models (GLLVMs). However, software for fitting these models is typically slow and not practical for large datasets.</jats:p></jats:list-item> <jats:list-item><jats:p>The <jats:sc>r</jats:sc> package <jats:styled-content>gllvm</jats:styled-content> offers relatively fast methods to fit GLLVMs via maximum likelihood, along with tools for model checking, visualization and inference.</jats:p></jats:list-item> <jats:list-item><jats:p>The main advantage of the package over other implementations is speed, for example, being two orders of magnitude faster, and capable of handling thousands of response variables. These advances come from using variational approximations to simplify the likelihood expression to be maximized, automatic differentiation software for model‐fitting (via the <jats:styled-content>TMB</jats:styled-content> package) and careful choice of initial values for parameters.</jats:p></jats:list-item> <jats:list-item><jats:p>Examples are used to illustrate the main features and functionality of the package, such as constrained or unconstrained ordination, including functional traits in ‘fourth corner’ models, and (if the number of environmental coefficients is not large) make inferences about environmental associations.</jats:p></jats:list-item> </jats:list> </jats:p>

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