

   FFiitt MMuullttiinnoommiiaall LLoogg--lliinneeaarr MMooddeellss

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

   AArrgguummeennttss::

    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 fac-
             tor with K > 2 classes, or a vector for a factor
             with 2 levels.  See the documentation of `formula'
             for further details.

       data: an optional data frame in which to interpret the
             variables occurring in the 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 dupli-
             cate rows and adjust weights.  Methods `1' and `2'
             differ in speed (`2' uses compiled C); method `3'
             also combines rows with the same X and different
             Y, which changes the baseline for the deviance.

        ...: additional arguments for `nnet'.

   VVaalluuee::

        A `nnet' object with additional structure.

   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).

   EExxaammpplleess::

        data(warpbreaks)
        options(contrasts=c("contr.treatment", "contr.poly"))
        multinom(breaks ~ wool, data=warpbreaks)

        ## S had -- VR data?
        bwt.mu <- multinom(low ~ ., bwt)
        bwt.mu
        ## 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

