Linear, Generalized, and Mixed/Multilevel models with R

Course philosophy

Introductory statistics are typically taught as a sequence of disconnected tests and protocols (e.g. t-test, ANOVA, ANCOVA, regression) while, in reality, all these analyses can be seen as special cases of a more general linear model. In this course, we will introduce Generalised Linear Models as a unified, coherent, and easily extendable framework for the analysis of many different types of data, including Normal (Gaussian), binary, and discrete (count) responses, and both categorical (factors) and continuous predictors.

Slides (PDF)

• Framework
• Introduction to linear models
• Linear models
• Variables and model selection
• Model comparison
• Generalised Linear Models for binary data
• Generalised Linear Models for count data
• Modelling zero-inflated count data
• Mixed effects / Multilevel models
• Generalised Additive Models (GAMs)
• An introduction to Bayesian modelling
• Causal inference
• Regression to the mean

Interactive tutorials, R scripts, etc

https://pakillo.github.io/LM-GLM-GLMM-intro/

LICENSE

These materials are released with a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. You can use/adapt them for non-commercial purposes as long as you mention the source (this repository) and share the materials with a similar license.

Francisco Rodriguez-Sanchez
https://frodriguezsanchez.net