multinom {nnet}  R Documentation 
Fits multinomial loglinear models via neural networks.
multinom(formula, data, weights, subset, na.action, contrasts = NULL, Hess = FALSE, summ = 0, censored = FALSE, model = FALSE, ...)
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 matrix with K columns
if the response is a matrix with K columns or a factor with K > 2
classes, or a vector for a factor with 2 levels.
See the documentation of formula() 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 > 2 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

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.
A nnet
object with additional components:
deviance 
the residual deviance. 
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).

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
options(contrasts = c("contr.treatment", "contr.poly")) library(MASS) example(birthwt) (bwt.mu < multinom(low ~ ., bwt)) ## Not run: Call: multinom(formula = low ~ ., data = bwt) Coefficients: (Intercept) age lwt raceblack raceother 0.823477 0.03724311 0.01565475 1.192371 0.7406606 smoke ptd ht ui ftv1 ftv2+ 0.7555234 1.343648 1.913213 0.6802007 0.4363238 0.1789888 Residual Deviance: 195.4755 AIC: 217.4755 ## End(Not run)