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Multinomial logistic regression values

` pihat = mnrval(B,X)` returns
the predicted probabilities for the multinomial logistic regression
model with predictors,

`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.

`[ pihat,dlow,dhi]
= mnrval(B,X,stats)` also
returns 95% error bounds on the predicted probabilities,

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.

`[ pihat,dlow,dhi]
= mnrval(B,X,stats,Name,Value)` returns
the predicted probabilities and 95% error bounds on the predicted
probabilities

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

`[ yhat,dlow,dhi]
= mnrval(B,X,ssize,stats)` also
computes 95% error bounds on the predicted counts

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.

`[ yhat,dlow,dhi]
= mnrval(B,X,ssize,stats,Name,Value)` returns
the predicted category counts and 95% error bounds on the predicted
counts

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

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

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