mnrval
Multinomial logistic regression values
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.