The outcome of a response variable might sometimes be one of a restricted set of possible values. If there are only two possible outcomes, such as male and female for gender, these responses are called binary responses. If there are multiple outcomes, then they are called polytomous responses. These responses are usually qualitative rather than quantitative, such as preferred districts to live in a city, the severity level of a disease, the species for a certain flower type, and so on. Polytomous responses might also have categories which are not independent of each other. Instead the response happens in a sequential manner, or one category is nested in the previous one. These types of responses are called hierarchical, or sequential, or nested multinomial responses.
For example, if the response is the number of cigarettes a person smokes in a given day, the first level is whether the person is a smoker or not. Given that he or she is a smoker, the number of cigarettes he or she smokes can be from one to five or more than five a day. Given that it is more than 5, this person might be smoking from 6 to 10 or more than 10 cigarettes a day, and so on. The risk group at each level changes accordingly. At level one, the risk group is all of the individuals of interest (smoker or not), say m. If out of m individuals, y1 of them are not smokers, then at level two, the risk group is the number of all smoking individuals, m – y1. If y2 of these m – y1 individuals smoke from one to five cigarettes a day, then at level three, the risk group is m – y1 – y2. So, at each level, the number of people in that category becomes a conditional binomial observation.
The hierarchical multinomial regression models are extensions
of binary regression models based on conditional binary observations.
The default is a model with different intercept and slopes (coefficients)
among categories, in which case
mnrfit fits a
sequence of conditional binomial models. The
pair specifies this in
mnrfit. The default link
function is logit and the
pair specifies this model in
Suppose the probability that an individual is in category j given that he or she is not in the previous categories is πj, and the cumulative probability that a response belongs to a category j or a previous category is P(y ≤ cj). Then the hierarchical model with a logit link function and different slopes assumption is
For example, for a response variable with four sequential categories, there are 4 – 1 = 3 equations as follows:
The coefficients βij are interpreted within each level. For example, for the previous smoking example, β12 shows the impact of X2 on the log odds of a person being a smoker versus a nonsmoker, provided that everything else is held constant. Alternatively, β22 shows the impact of X2 on the log odds of a person smoking one to five cigarettes versus more than five cigarettes a day, given that he or she is a smoker, provided that everything else is held constant. Similarly, β23, shows the effect of X2 on the log odds of a person smoking 6 to 10 cigarettes versus more than 10 cigarettes a day, given that he or she smokes more than 5 cigarettes a day, provided that everything else is held constant.
You can specify other link functions for hierarchical models.
'link','probit' name-value pair argument uses
the probit link function. With the separate slopes assumption, the
where πj is the conditional probability of being in category j, given that it is not in categories previous to category j. And Φ-1(.) is the inverse of the standard normal cumulative distribution function.
After estimating the model coefficients using
you can estimate the cumulative probabilities or the cumulative number
in each category using
mnrval with the
pair argument. The function
mnrval 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 specify which category
or cumulative probabilities or numbers to estimate by changing the
value of the
'type' name-value pair argument in
 McCullagh, P., and J. A. Nelder. Generalized Linear Models. New York: Chapman & Hall, 1990.
 Liao, T. F. Interpreting Probability Models: Logit, Probit, and Other Generalized Linear Models Series: Quantitative Applications in the Social Sciences. Sage Publications, 1994.