Tutorial: GAMs and other smooth GLMs with R

Generalized
Additive Models (GAMs) are Generalized Linear Models (GLMs) in which
the linear predictor is specified partly in terms of a sum of smooth
functions
of covariates. Generalizing a little further, it is possible to include random effects terms in the linear predictor, yielding Generalized Additive Mixed Models (GAMM), and also to include linear functionals of smooths as terms (leading to signal regression models, functional GLMs, varying coefficient models etc.). Representing the smooth functions using low rank splines (Psplines or classical penalized regression splines) leads to a computationally convenient and fairly complete framework for such modelling, in particular allowing reliable estimation of the appropriate degree of smoothness for each component function. The workshop will give an outline of this penalized regression spline approach to penalized GLMs as implemented in R package mgcv, and will discuss issues of model building, checking and inference, in practice. 