Introduction to mixed modelling and GLMM -Bayesian and frequentist approaches-

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The course starts with a basic introduction to linear mixed effects models, followed by an introduction to Bayesian statistics, MCMC and generalised linear mixed effects models (GLMM) to analyse nested (also called hierarchical or clustered) data, e.g. multiple observations from the same animal, site, area, nest, patient, hospital, vessel, lake, hive, transect, etc.

During the course several case studies are presented, in which the statistical theory for mixed models is integrated with applied analyses in a clear and understandable manner. Throughout the course MCMC is executed in JAGS (free software) via the package R2jags from within R. Bayesian and frequentist (lme4, nlme, glmmTMB) analyses are compared.


Introduction to linear mixed effects models, GLMM, Bayesian statistics and MCMC. JAGS, R2jags, lme4, nlme and glmmTMB. Nested data. Dealing with pseudo-replication. Bayesian model selection.