Generalized Linear Models 1: Logistic regression

In this session we will discuss what are Generalized Linear Models and practice the special case of logistic regression.

Exercises will cover:

• Fitting and interpreting a simple logistic regression with the function glm() in R
• Producing model predictions for logistic regression
• Clustering predictions and assessing their performance
• Random effect and repeatability for a logistic regression

Download all the material here: https://github.com/timotheenivalis/RSB-R-Stats-Biology/raw/master/Archive/08.LogisticRegression/Material.zip.

Answers to exercises are here: https://github.com/timotheenivalis/RSB-R-Stats-Biology/raw/master/Archive/08.LogisticRegression/Source/glm1exercisesANSWERS.pdf. (clicking this link should trigger download of the pdf)

The code written during the workshop is here: https://github.com/timotheenivalis/RSB-R-Stats-Biology/blob/master/Archive/08.LogisticRegression/Script.R.