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gllvm : Fast analysis of multivariate abundance data with generalized linear latent variable models in r
Author(s) -
Niku Jenni,
Hui Francis K. C.,
Taskinen Sara,
Warton David I.
Publication year - 2019
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13303
Subject(s) - latent variable , inference , multivariate statistics , computer science , software , r package , variable (mathematics) , software package , statistical inference , expression (computer science) , generalized linear model , data mining , algorithm , mathematics , statistics , machine learning , artificial intelligence , computational science , mathematical analysis , programming language
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. The r package gllvm offers relatively fast methods to fit GLLVMs via maximum likelihood, along with tools for model checking, visualization and inference. 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 TMB package) and careful choice of initial values for parameters. 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.