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 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.
nnet
oc <- options(contrasts = c("contr.treatment", "contr.poly")) library(MASS) example(birthwt) (bwt.mu <- multinom(low ~ ., bwt)) options(oc)