multinom
From nnet v7.312
by Brian Ripley
Fit Multinomial Loglinear Models
Fits multinomial loglinear models via neural networks.
Usage
multinom(formula, data, weights, subset, na.action, contrasts = NULL, Hess = FALSE, summ = 0, censored = FALSE, model = FALSE, ...)
Arguments
 formula

a formula expression as for regression models, of the form
response ~ predictors
. The response should be a factor or a matrix with K columns, which will be interpreted as counts for each of K classes. A loglinear model is fitted, with coefficients zero for the first class. An offset can be included: it should be a numeric matrix with K columns if the response is either a matrix with K columns or a factor with K >= 2 classes, or a numeric vector for a response factor with 2 levels. See the documentation offormula()
for other details.  data

an optional data frame in which to interpret the variables occurring
in
formula
.  weights
 optional case weights in fitting.
 subset
 expression saying which subset of the rows of the data should be used in the fit. All observations are included by default.
 na.action
 a function to filter missing data.
 contrasts
 a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
 Hess
 logical for whether the Hessian (the observed/expected information matrix) should be returned.
 summ

integer; if nonzero summarize by deleting duplicate rows and adjust weights.
Methods 1 and 2 differ in speed (2 uses
C
); method 3 also combines rows with the same X and different Y, which changes the baseline for the deviance.  censored

If Y is a matrix with
K
columns, interpret the entries as one for possible classes, zero for impossible classes, rather than as counts.  model

logical. If true, the model frame is saved as component
model
of the returned object.  ...

additional arguments for
nnet
Details
multinom
calls nnet
. The variables on the rhs of
the formula should be roughly scaled to [0,1] or the fit will be slow
or may not converge at all.
Value

A
 deviance
 the residual deviance, compared to the full saturated model (that explains individual observations exactly). Also, minus twice loglikelihood.
 edf
 the (effective) number of degrees of freedom used by the model
 AIC
 the AIC for this fit.
 Hessian

(if
Hess
is true).  model

(if
model
is true).
nnet
object with additional components:References
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
See Also
Examples
library(nnet)
options(contrasts = c("contr.treatment", "contr.poly"))
library(MASS)
example(birthwt)
(bwt.mu < multinom(low ~ ., bwt))
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