# multinom

From nnet v7.3-0
by Brian Ripley

##### Fit Multinomial Log-linear Models

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 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: 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).

##### concept

multiple logistic

##### References

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

##### See Also

##### 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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