Ordinal Regression is used to predict ranked values. In simple words, this type of regression is suitable when dependent variable is ordinal in nature. Example of ordinal variables – Survey responses (1 to 6 scale), patient reaction to drug dose (none, mild, severe).
Why we can’t use linear regression when dealing with ordinal target variable?
In linear regression, the dependent variable assumes that changes in the level of the dependent variable are equivalent throughout the range of the variable. For example, the difference in weight between a person who is 100 kg and a person who is 120 kg is 20kg, which has the same meaning as the difference in weight between a person who is 150 kg and a person who is 170 kg. These relationships do not necessarily hold for ordinal variables.
o.model <- clm(rating ~ ., data = wine) summary(o.model)