# multinom {nnet}

### 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.

### Values

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

### Examples

Documentation reproduced from package nnet, version 7.3-12. License: GPL-2 | GPL-3