## 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, also called a *multinomial logit* model, is one
of several natural extensions of the binary logit model. A multinomial logit model
explains the relative risk of an observation 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 variables. The `fitmnr`

function specifies this multinomial model for nominal responses. By default,
`fitmnr`

specifies separate intercepts and slopes among
categories, and uses the `logit`

link function to create a `MultinomialRegression`

model object. You cannot specify a different link
function for nominal responses.

The multinomial logit model is

$$\begin{array}{l}\mathrm{ln}\left(\frac{{\pi}_{1}}{{\pi}_{k}}\right)={\alpha}_{1}+{\beta}_{11}{X}_{1}+{\beta}_{12}{X}_{2}+\cdots +{\beta}_{1p}{X}_{p},\\ \mathrm{ln}\left(\frac{{\pi}_{2}}{{\pi}_{k}}\right)={\alpha}_{2}+{\beta}_{21}{X}_{1}+{\beta}_{22}{X}_{2}+\cdots +{\beta}_{2p}{X}_{p},\\ \text{\hspace{1em}}\text{\hspace{0.17em}}\vdots \\ \mathrm{ln}\left(\frac{{\pi}_{k-1}}{{\pi}_{k}}\right)={\alpha}_{(k-1)}+{\beta}_{(k-1)1}{X}_{1}+{\beta}_{(k-1)2}{X}_{2}+\cdots +{\beta}_{(k-1)p}{X}_{p},\end{array}$$

where *π*_{j
} = 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
`fitmnr`

selects the final category *k* as the
reference category. Therefore, `fitmnr`

assumes that the coefficients
of the *k*th category are zero. The total of *j* – 1
equations are solved simultaneously to estimate the coefficients.
`fitmnr`

uses the iteratively weighted least-squares algorithm to
find the maximum likelihood estimates.

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 *β*_{23} indicates
that the probability of the response variable being in category 2
compared to the probability of being in category *k* increases
exp(*β*_{23}) times for
each unit increase in *X*_{3},
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 *β*_{23} times
with a one-unit increase in *X*_{3},
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

$${\pi}_{j}=P\left(y=j\right)=\frac{{e}^{{\alpha}_{j}+{\displaystyle \sum _{l=1}^{p}{\beta}_{jl}{x}_{l}}}}{1+{\displaystyle \sum _{j=1}^{k-1}{e}^{{\alpha}_{j}+{\displaystyle \sum _{l=1}^{p}{\beta}_{jl}{x}_{l}}}}},\text{\hspace{1em}}j=1,\cdots ,k-1.$$

The probability of the *k*th category becomes

$${\pi}_{k}=P\left(y=k\right)=\frac{1}{1+{\displaystyle \sum _{j=1}^{k-1}{e}^{{\alpha}_{j}+{\displaystyle \sum _{l=1}^{p}{\beta}_{jl}{x}_{l}}}}},$$

which is simply equal to 1 – *π*_{1} – *π*_{2} –
... – *π*_{k–1}.

After estimating the model coefficients by using `fitmnr`

to create
a `MultinomialRegression`

model object, you can estimate the category
probabilities by using `predict`

.
This function accepts the `MultinomialRegression`

model object returned
by `fitmnr`

, and estimates the category labels, categorical
probabilities, and confidence bounds for each categorical probability. You can specify
whether `predict`

returns category, cumulative, or conditional
probabilities using the `ProbabilityType`

name-value argument.

## References

[1] McCullagh, P., and J. A. Nelder. *Generalized
Linear Models*. New York: Chapman & Hall, 1990.

[2] Long, J. S. *Regression Models for Categorical
and Limited Dependent Variables*. Sage Publications, 1997.

[3] Dobson, A. J., and A. G. Barnett. *An Introduction
to Generalized Linear Models*. Chapman and Hall/CRC. Taylor
& Francis Group, 2008.

## See Also

`fitglm`

| `fitmnr`

| `predict`

| `glmfit`

| `glmval`