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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, y_{1} of them are not smokers, then at level two, the risk group is the number of all smoking individuals, m – y_{1}. If y_{2} of these m – y_{1} individuals smoke from one to five cigarettes a day, then at level three, the risk group is m – y_{1} – y_{2}. 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 'interactions','on' name-value pair specifies this in mnrfit. The default link function is logit and the 'link','logit' name-value pair specifies this model in mnrfit.
Suppose the probability that an individual is in category j given that he or she is not in the previous categories is π_{j}, and the cumulative probability that a response belongs to a category j or a previous category is P(y ≤ c_{j}). Then the hierarchical model with a logit link function and different slopes assumption is
$$\begin{array}{l}\mathrm{ln}\left(\frac{{\pi}_{1}}{1-P\left(y\le {c}_{1}\right)}\right)=\mathrm{ln}\left(\frac{{\pi}_{1}}{1-{\pi}_{1}}\right)={\alpha}_{1}+{\beta}_{11}{X}_{1}+{\beta}_{12}{X}_{2}+\cdots +{\beta}_{1p}{X}_{p},\\ \mathrm{ln}\left(\frac{{\pi}_{2}}{1-P\left(y\le {c}_{2}\right)}\right)=\mathrm{ln}\left(\frac{{\pi}_{2}}{1-\left({\pi}_{1}+{\pi}_{2}\right)}\right)={\alpha}_{2}+{\beta}_{21}{X}_{2}+{\beta}_{22}{X}_{2}+\cdots +{\beta}_{2p}{X}_{p},\\ \text{\hspace{1em}}\text{\hspace{1em}}\vdots \\ \mathrm{ln}\left(\frac{{\pi}_{k-1}}{1-P\left(y\le {c}_{k-1}\right)}\right)=\mathrm{ln}\left(\frac{{\pi}_{k-1}}{1-\left({\pi}_{1}+\cdots +{\pi}_{k-1}\right)}\right)={\alpha}_{k-1}+{\beta}_{(k-1)1}{X}_{1}+{\beta}_{(k-1)2}{X}_{2}+\cdots +{\beta}_{(k-1)p}{X}_{p}.\end{array}$$
For example, for a response variable with four sequential categories, there are 4 – 1 = 3 equations as follows:
$$\begin{array}{l}\mathrm{ln}\left(\frac{\pi {}_{1}}{\pi {}_{2}+\pi {}_{3}+\pi {}_{4}}\right)={\alpha}_{1}+{\beta}_{11}{X}_{1}+{\beta}_{12}{X}_{2}+\cdots +{\beta}_{1p}{X}_{p},\\ \mathrm{ln}\left(\frac{\pi {}_{2}}{\pi {}_{3}+\pi {}_{4}}\right)={\alpha}_{2}+{\beta}_{21}{X}_{1}+{\beta}_{22}{X}_{2}+\cdots +{\beta}_{2p}{X}_{p},\\ \mathrm{ln}\left(\frac{\pi {}_{3}}{\pi {}_{4}}\right)={\alpha}_{3}+{\beta}_{31}{X}_{1}+{\beta}_{32}{X}_{2}+\cdots +{\beta}_{3p}{X}_{p}.\end{array}$$
The coefficients β_{ij} are interpreted within each level. For example, for the previous smoking example, β_{12} shows the impact of X_{2} on the log odds of a person being a smoker versus a nonsmoker, provided that everything else is held constant. Alternatively, β_{22} shows the impact of X_{2} 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, β_{23}, shows the effect of X_{2} 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. The 'link','probit' name-value pair argument uses the probit link function. With the separate slopes assumption, the model becomes
$$\begin{array}{l}{\Phi}^{-1}\left({\pi}_{1}\right)={\alpha}_{1}+{\beta}_{11}{X}_{1}+\cdots +{\beta}_{1p}{X}_{p},\text{\hspace{1em}}\\ {\Phi}^{-1}\left({\pi}_{2}\right)={\alpha}_{2}+{\beta}_{21}{X}_{1}+\cdots +{\beta}_{2p}{X}_{p},\\ \text{\hspace{1em}}\text{\hspace{1em}}\vdots \text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\vdots \\ {\Phi}^{-1}\left({\pi}_{k}\right)={\alpha}_{k}+{\beta}_{k1}{X}_{1}+\cdots +{\beta}_{kp}{X}_{p},\end{array}$$
where π_{j} 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 mnrfit, you can estimate the cumulative probabilities or the cumulative number in each category using mnrval with the 'type','conditional' name-value pair argument. The function mnrval accepts the coefficient estimates and the model statistics mnrfit returns, 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 mnrval.
[1] McCullagh, P., and J. A. Nelder. Generalized Linear Models. New York: Chapman & Hall, 1990.
[2] Liao, T. F. Interpreting Probability Models: Logit, Probit, and Other Generalized Linear Models Series: Quantitative Applications in the Social Sciences. Sage Publications, 1994.
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