21.1 Ordinal Probit Regression
21.1.1 What the Data Look Like
21.1.2 The Mapping from Metric x to Ordinal y
21.1.3 The Parameters and Their Priors
21.1.4 Standardizing for MCMC Efficiency
21.1.5 Posterior Prediction
21.2 Some Examples
21.2.1 Why Are Some Thresholds Outside the Data?
21.4 Relation to Linear and Logistic Regression
21.5 R Code
The winner is first, and that’s all that he knows, whether
Won by a mile or won by a nose. But
Second recalls every inch of that distance, in
Vivid detail and with haunting persistence.
Very often the predicted variable is ordinal, such as a rating on a scale from 1 to 5. Rate how much you agree with this statement: “Bayesian methods ...