multinom

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Fit Multinomial Log-linear Models

Fits multinomial log-linear models via neural networks.

Keywords
models, neural
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.

nnet

Aliases
• multinom
• anova.multinom
• coef.multinom
• drop1.multinom
• extractAIC.multinom
• predict.multinom
• print.multinom
• summary.multinom
• print.summary.multinom
• vcov.multinom
• model.frame.multinom
• logLik.multinom
Examples
# NOT RUN {
oc <- options(contrasts = c("contr.treatment", "contr.poly"))
library(MASS)
example(birthwt)
(bwt.mu <- multinom(low ~ ., bwt))
options(oc)
# }

Documentation reproduced from package nnet, version 7.3-14, License: GPL-2 | GPL-3

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