Multinomial Models for Nominal Responses
The outcome of a response variable might be one of a restricted set of possible values. If there are only two possible outcomes, such as a yes or no answer to a question, these responses are called binary responses. If there are multiple outcomes, then they are called polytomous responses. Some examples include the degree of a disease (mild, medium, severe), preferred districts to live in a city, and so on. When the response variable is nominal, there is no natural order among the response variable categories. Nominal response models explain and predict the probability that an observation is in each category of a categorical response variable.
A nominal response model is one of several natural extensions
of the binary logit model and is also called a multinomial
logit model. The multinomial logit model explains the relative
risk of being in one category versus being in the reference category, k,
using a linear combination of predictor variables. Consequently, the
probability of each outcome is expressed as a nonlinear function of p predictor
'interactions','on' name-value pair
mnrfit corresponds to this multinomial
model with separate intercept and slopes among categories.
the default logit link function for multinomial models. You cannot
specify a different link function for multinomial responses.
The multinomial logit model is
where πj =
P(y = j) is the probability
of an outcome being in category j, k is
the number of response categories, and p is the
number of predictor variables. Theoretically, any category can be
the reference category, but
mnrfit chooses the
last one, k, as the reference category. Thus,
the coefficients of the kth category are zero.
The total of j – 1 equations are solved
simultaneously to estimate the coefficients.
the iteratively weighted least squares algorithm to find the maximum
The coefficients in the model express the effects of the predictor variables on the relative risk or the log odds of being in category j versus the reference category, here k. For example, the coefficient β23 indicates that the probability of the response variable being in category 2 compared to the probability of being in category k increases exp(β23) times for each unit increase in X3, given all else is held constant. Or it indicates that the relative log odds of the response variable being category 2 versus in category k increases β23 times with a one-unit increase in X3, given all else equal.
Based on the nominal response model, and the assumption that the coefficients for the last category are zero, the probability of being in each category is
The probability of the kth category becomes
which is simply equal to 1 – π1 – π2 – ... – πk–1.
After estimating the model coefficients using
you can estimate the category probabilities or the number in each
mnrval (the default name-value pair
'type','category'). This function accepts the
coefficient estimates and the model statistics
and estimates the categorical probabilities or the number in each
category and their confidence bounds. You can also specify the cumulative
or conditional probabilities or numbers to estimate using the
pair argument in
 McCullagh, P., and J. A. Nelder. Generalized Linear Models. New York: Chapman & Hall, 1990.
 Long, J. S. Regression Models for Categorical and Limited Dependent Variables. Sage Publications, 1997.
 Dobson, A. J., and A. G. Barnett. An Introduction to Generalized Linear Models. Chapman and Hall/CRC. Taylor & Francis Group, 2008.