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

by Benjamin M Bolker, Benjamin M Bolker, Mollie E Brooks, Mollie E Brooks, Connie J Clark, Connie J Clark, Shane W Geange, Shane W Geange, John R Poulsen, John R Poulsen, M Henry H Stevens, M Henry H Stevens, Jada-Simone S White, Jada-Simone S White show all authors
Trends in Ecology and Evolution ()

Abstract

How should ecologists and evolutionary biologists\ranalyze nonnormal data that involve random effects?\rNonnormal data such as counts or proportions often defy\rclassical statistical procedures. Generalized linear mixed\rmodels (GLMMs) provide a more flexible approach for\ranalyzing nonnormal data when random effects are present.\rThe explosion of research on GLMMs in the last\rdecade has generated considerable uncertainty for practitioners\rin ecology and evolution. Despite the availability\rof accurate techniques for estimating GLMM parameters\rin simple cases, complex GLMMs are challenging to fit\rand statistical inference such as hypothesis testing\rremains difficult. We review the use (and misuse) of\rGLMMs in ecology and evolution, discuss estimation\randinference andsummarize ‘best-practice’ data analysis\rprocedures for scientists facing this challenge.

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