multinom

From nnet v7.3-0 by Brian Ripley

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 coeffi
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

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:
deviancethe residual deviance, compared to the full saturated model (that explains individual observations exactly). Also, minus twice log-likelihood.
edfthe (effective) number of degrees of freedom used by the model
AICthe AIC for this fit.
Hessian(if Hess is true).
model(if model is true).

concept

multiple logistic

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))
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

Documentation reproduced from package nnet, version 7.3-0, License: GPL-2 | GPL-3

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