gllvm : Fast analysis of multivariate abundance data with generalized linear latent variable models in R
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
Published inMethods in Ecology and Evolution
© 2019 The Authors. Methods in Ecology and Evolution © 2019 British Ecological Society
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. ...
Publication in research information system
MetadataShow full item record
Additional information about fundingThe work of JN was supported by the Wihuri Foundation. The work of ST was supported by the CRoNoS COST Action IC1408. The work of FKCH and DIW was supported by ustralia Research Council Discovery Project grants (DP180100836 and DP150100823, espectively), FKCH was also supported by an ANU cross disciplinary grant.
Showing items with similar title or keywords.
Analyzing environmental‐trait interactions in ecological communities with fourth‐corner latent variable models Niku, Jenni; Hui, Francis K. C.; Taskinen, Sara; Warton, David I. (John Wiley & Sons, 2021)In ecological community studies it is often of interest to study the effect of species related trait variables on abundances or presence-absences. Specifically, the interest may lay in the interactions between environmental ...
Niku, Jenni; Brooks, Wesley; Herliansyah, Riki; Hui, Francis K. C.; Taskinen, Sara; Warton, David I. (Public Library of Science, 2019)Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, ...
Hui, Francis K. C.; Warton, David I.; Ormerod, John T.; Haapaniemi, Viivi; Taskinen, Sara (American Statistical Association, 2017)Generalized linear latent variable models (GLLVMs) are a powerful class of models for understanding the relationships among multiple, correlated responses. Estimation, however, presents a major challenge, as the marginal ...
Fitting Generalized Linear Latent Variable Models using the method of Extended Variational Approximation Korhonen, Pekka (2020)Yhteisöekologian alalla tutkijat ovat usein kiinnostuneita yhden tai useamman kasvi- tai eläinlajin välisistä esiintyvyyssuhteista eri mittauspaikoilla tai ekosysteemeissä. Tämänkaltaiset tutkimuskysymykset johtavat ...
Helske, Satu (University of Jyväskylä, 2016)