Multinomial logistic regression values
pihat = mnrval(B,X)
[pihat,dlow,dhi]
= mnrval(B,X,stats)
[pihat,dlow,dhi]
= mnrval(B,X,stats,Name,Value)
yhat = mnrval(B,X,ssize)
[yhat,dlow,dhi]
= mnrval(B,X,ssize,stats)
[yhat,dlow,dhi]
= mnrval(B,X,ssize,stats,Name,Value)
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 nbyk 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 nbyp matrix which contains n observations
for p predictors.
Note:

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