A generalization of logistic regression techniques makes it possible to deal with the case where the dependent variable is categorical on more than two levels. This is a case of multinomial or polynomial logistic regression.
A first distinction to operate is between nominal and ordinal logistic regression. We refer to nominal logistic regression when there is no natural order among the categories of the dependent variable, as can be the choice between four pizza types or between some singers. When, on the other hand, you can classify the dependent variable levels in an orderly scale, you are talking about ordinal logistic regression.
To perform multinomial logistic regression analysis, we can use the mlogit package. mlogit is a package for R which enables the estimation of the multinomial logit models with individual and/or alternative specific...
