20.1 Logistic Regression
20.1.1 The Model
20.1.2 Doing It in R and BUGS
20.1.3 Interpreting the Posterior
20.1.4 Perils of Correlated Predictors
20.1.5 When There Are Few 1’s in the Data
20.1.6 Hyperprior Across Regression Coefficients
20.2 Interaction of Predictors in Logistic Regression
20.3 Logistic ANOVA
20.3.1 Within-Subject Designs
20.5 R Code
20.5.1 Logistic Regression Code
20.5.2 Logistic ANOVA Code
Fortune and Favor make fickle decrees, it’s
Heads or it’s tails with no middle degrees.
Flippant commandments decreed by law gods, have
Reasons so rare they have minus log odds.
In many situations the value to be predicted is dichotomous (instead of metric). For example, ...