Fit Multinomial Log-linear Models

DESCRIPTION:

Fits multinomial log-linear models via neural networks.

USAGE:

multinom(formula, data=sys.parent(), weights, subset, na.action,
contrasts=NULL, Hess=F, summ=0, censored=F, ...)

REQUIRED 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 log-linear 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.

OPTIONAL ARGUMENTS:

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 information matrix) should be returned.
summ
integer; if non-zero 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.
...
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 nnet object with additional slots.
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).
NB: this is an object of formal class "multinom"

SEE ALSO:

EXAMPLES:

library(MASS)  # To get birthwt dataset
bwt.mu <- multinom(low ~ ., birthwt)