# mnrval

(Not recommended) Multinomial logistic regression values

`mnrval`

is not recommended.
Instead, use `fitmnr`

to create a `MultinomialRegression`

model object and then use the `predict`

object function.* (since R2023a)* For more information see Version History.

## Syntax

## Description

returns the predicted probabilities for the multinomial logistic regression
model with predictors, `pihat`

= mnrval(`B`

,`X`

)`X`

, and the coefficient estimates,
`B`

.

`pihat`

is an
*n*-by-*k* matrix of predicted
probabilities for each multinomial category. `B`

is the vector
or matrix that contains the coefficient estimates returned by `mnrfit`

. And `X`

is
an *n*-by-*p* matrix which contains
*n* observations for *p*
predictors.

**Note**

`mnrval`

automatically includes a constant term in
all models. Do not enter a column of 1s in `X`

.

`[`

also returns 95% error bounds on the predicted probabilities,
`pihat`

,`dlow`

,`dhi`

]
= mnrval(`B`

,`X`

,`stats`

)`pihat`

, using the statistics in the structure,
`stats`

, returned by `mnrfit`

.

The lower and upper confidence bounds for `pihat`

are
`pihat`

minus `dlow`

and
`pihat`

plus `dhi`

, respectively.
Confidence bounds are nonsimultaneous and only apply to the fitted curve, not to
new observations.

`[`

returns the predicted probabilities and 95% error bounds on the predicted
probabilities `pihat`

,`dlow`

,`dhi`

]
= mnrval(`B`

,`X`

,`stats`

,`Name,Value`

)`pihat`

, with additional options specified by one
or more `Name,Value`

pair arguments.

For example, you can specify the model type, link function, and the type of probabilities to return.

`[`

also computes 95% error bounds on the predicted counts `yhat`

,`dlow`

,`dhi`

]
= mnrval(`B`

,`X`

,`ssize`

,`stats`

)`yhat`

,
using the statistics in the structure, `stats`

, returned by
`mnrfit`

.

The lower and upper confidence bounds for `yhat`

are
`yhat`

minus `dlo`

and
`yhat`

plus `dhi`

, respectively.
Confidence bounds are nonsimultaneous and they apply to the fitted curve, not to
new observations.

`[`

returns the predicted category counts and 95% error bounds on the predicted
counts `yhat`

,`dlow`

,`dhi`

]
= mnrval(`B`

,`X`

,`ssize`

,`stats`

,`Name,Value`

)`yhat`

, with additional options specified by one or more
`Name,Value`

pair arguments.

For example, you can specify the model type, link function, and the type of predicted counts to return.

## Examples

## Input Arguments

## Output Arguments

## References

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

## Version History

**Introduced in R2006b**