Generalized Additive Models: Applications and Techniques

This lecture covers Generalized Additive Models (GAMs), starting with the basics of linear models and multiple regression. It then delves into the concept of GAMs, explaining how they extend GLMs by incorporating smooth functions of covariates. The lecture demonstrates the use of polynomial and cubic spline bases for modeling smooth functions, along with controlling model smoothness through penalties. Practical examples using the mgcv package in R are provided to illustrate fitting GAMs and visualizing the results. Additionally, the lecture introduces linear mixed models as an extension of the linear model, incorporating random effects. The instructor emphasizes practical applications through examples and code snippets.