A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Niku, Jenni; Hui, Francis K.C.; Taskinen, Sara; Warton, David I.

Lehti tai sarja: Methods in Ecology and Evolution

eISSN: 2041-210X

Julkaisuvuosi: 2019

Volyymi: 10

Lehden numero: 12

Artikkelin sivunumerot: 2173-2182

Kustantaja: Wiley

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (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.

YSO-asiasanat: monimuuttujamenetelmät; mallit (mallintaminen); mallintaminen; tilastolliset mallit; lajit; vuorovaikutus; ekologia

Vapaat asiasanat: high-dimensional data; joint modelling; multivariate analysis; or-26dination; species interactions

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2019

JUFO-taso: 2

Viimeisin päivitys 2021-16-07 klo 10:39