Sign up & Download
Sign in

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 ()


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.

Cite this document (BETA)

Readership Statistics

1 Reader on Mendeley
by Discipline
by Academic Status
100% Researcher (at a non-Academic Institution)

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Already have an account? Sign in