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 > 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:

`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.

### See Also

`nnet`

### Examples

    options(contrasts = c("contr.treatment", "contr.poly"))
    library(MASS)
    example(birthwt)
    (bwt.mu <- multinom(low ~ ., bwt))
    ## Not run: 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

    ## End(Not run)

[Package nnet version 7.3-1 Index]