On modeling multivariate abundance data with generalized linear latent variable models
Publiceringsår
2020
Upphovspersoner
Niku, Jenni
Abstrakt
The multivariate abundance data consist typically of multiple, correlated species encountered at a set of sites, together with records of additional covariates. When analysing such data, model-based approaches have been shown to outperform classical algorithmic-based dimension reduction methods. In this thesis we consider generalized linear latent variable models, which offer a general framework for the analysis of multivariate abundance data. In order to make the models more attractive among practitioners, new computationally efficient algorithms for the parameter estimation are developed by applying closed form approximation methods, the variational approximation method and the Laplace approximation method, for the marginal likelihood and by utilizing automatic differentiation tools when implementing the algorithms. The accuracy and computational efficiency of the methods are investigated and compared to existing methods through extensive simulation studies. The developed algorithms and additional tools implemented for model diagnosis, visualization and statistical inference are collected in R package gllvm. Several examples are provided to illustrate the use of the generalized linear latent variable models in ordination and when studying the between-species correlations and the effects of environmental variables, trait variables and their interactions on ecological communities.
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Publikationstyp
Publikationsform
Separat verk
Målgrupp
Vetenskaplig
UKM:s publikationstyp
G5 Artikelavhandling
Publikationskanalens uppgifter
Öppen tillgång
Öppen tillgänglighet i förläggarens tjänst
Ja
Öppen tillgång till publikationskanalen
Helt öppen publikationskanal
Parallellsparad
Nej
Övriga uppgifter
Vetenskapsområden
Statistik; Ekologi, evolutionsbiologi
Nyckelord
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Publiceringsland
Finland
Förlagets internationalitet
Inhemsk
Språk
engelska
Internationell sampublikation
Nej
Sampublikation med ett företag
Nej
Publikationen ingår i undervisnings- och kulturministeriets datainsamling
Ja