multinom {nnet}R Documentation

Fit Multinomial Log-linear Models


Fits multinomial log-linear 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 log-linear 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 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 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.
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, compared to the full saturated model (that explains individual observations exactly). Also, minus twice log-likelihood.
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.

See Also



options(contrasts = c("contr.treatment", "contr.poly"))
( <- multinom(low ~ ., bwt))
## Not run: Call:
multinom(formula = low ~ ., data = bwt)

 (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)

[Package nnet version 7.2-33 Index]