Introduction to GAM and GAMM with R   

-Bayesian and frequentist approaches-


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

Sometimes, parametric models do not quite fit the data and in such cases generalised additive models (GAM; a smoothing technique) can be used. We will explain and illustrate GAMs to analyse continuous data, count data and binary data.

In the second part of the course we use generalised additive mixed effects models (GAMM) 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 is integrated with applied analyses in a clear and understandable manner. We will use frequentist (mgcv, gamm4) and Bayesian tools (MCMCin JAGS).


Gaussian, Poisson, negative binomial, and Bernoulli GAM and GAMM. Implementation of smoothers with MCMC. JAGS. R2jags. Nested data. Dealing with pseudo-replication. Adding spatial dependency to GAMs using INLA.