R: Fit Multinomial Log-linear Models

multinom {nnet}

R Documentation

Fit Multinomial Log-linear Models

Description

Fits multinomial log-linear models via neural networks.

Usage

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

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 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`

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 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).

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

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

[Package nnet version 7.3-12 Index]