A1 Journal article (refereed)
gllvm : Fast analysis of multivariate abundance data with generalized linear latent variable models in R (2019)

Niku, J., Hui, F. K., Taskinen, S., & Warton, D. I. (2019). gllvm : Fast analysis of multivariate abundance data with generalized linear latent variable models in R. Methods in Ecology and Evolution, 10(12), 2173-2182. https://doi.org/10.1111/2041-210X.13303

JYU authors or editors

Publication details

All authors or editors: Niku, Jenni; Hui, Francis K.C.; Taskinen, Sara; Warton, David I.

Journal or series: Methods in Ecology and Evolution

eISSN: 2041-210X

Publication year: 2019

Volume: 10

Issue number: 12

Pages range: 2173-2182

Publisher: Wiley

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1111/2041-210X.13303

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65596


1.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 datsets.

2.The R package gllvm offers relatively fast methods to fit GLLVMs via maximum likelihood, along with tools for model checking, visualization and inference.

3.The main advantage of the package over other implementations is speed e.g. 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 maximised, automatic differentiation software for model‐fitting (via the TMB package), and careful choice of initial values for parameters.

4.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.

Keywords: multivariable methods; models (objects); modelling (creation related to information); statistical models; types and species; interaction; ecology

Free keywords: high-dimensional data; joint modelling; multivariate analysis; or-26dination; species interactions

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 2

Last updated on 2022-14-09 at 11:46