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Generalized linear mixed models: a practical guide for ecology and evolution


Bolker, Benjamin M; Brooks, Mollie E; Clark, Connie J; Geange, Shane W; Poulsen, John R; Stevens, M Henry H; White, Jada-Simone S (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution, 24(3):127-135.

Abstract

How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are pre- sent. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for prac- titioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize ‘best-practice’ data analysis procedures for scientists facing this challenge.

Abstract

How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are pre- sent. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for prac- titioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize ‘best-practice’ data analysis procedures for scientists facing this challenge.

Citations

1915 citations in Web of Science®
2245 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Language:English
Date:2009
Deposited On:12 Mar 2014 10:06
Last Modified:05 Apr 2016 17:23
Publisher:Elsevier
ISSN:0169-5347
Publisher DOI:https://doi.org/10.1016/j.tree.2008.10.008
PubMed ID:19185386

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