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
Abstract
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 (representation); 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