Introduction to mixed modelling and GLMM -Frequentist approaches-

The course material is accessible via the menu on the left.

The course starts with a basic introduction to linear mixed effects models, followed by an introduction to  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 mixed models are executed in nlme, lme4 and glmmTMB.


Introduction to linear mixed effects models, GLMM, lme4, nlme and glmmTMB. Nested data. Dealing with pseudo-replication.