# Multinomial (Baseline) Logit Models for Categorical and Multinommial Responses¶

## Description¶

The function `mblogit`

fits multinomial logit models for categorical
and multinomial count responses with fixed alternatives, where the
logits are relative to a baseline category.

## Usage¶

```
mblogit(formula, data = parent.frame(), random = NULL, subset,
weights = NULL, na.action = getOption("na.action"), model = TRUE,
x = FALSE, y = TRUE, contrasts = NULL, control = mclogit.control(...),
...)
```

## Arguments¶

`formula`

the model formula. The response must be a factor or a matrix of counts.

`data`

an optional data frame, list or environment (or object coercible by

`as.data.frame`

to a data frame) containing the variables in the model. If not found in`data`

, the variables are taken from`environment(formula)`

, typically the environment from which`glm`

is called.`random`

an optional formula that specifies the random-effects structure or NULL.

`subset`

an optional vector specifying a subset of observations to be used in the fitting process.

`weights`

an optional vector of weights to be used in the fitting process. Should be

`NULL`

or a numeric vector.`na.action`

a function which indicates what should happen when the data contain

`NA``s. The default is set by the ``na.action`

setting of`options`

, and is`na.fail`

if that is unset. The ‘factory-fresh’ default is`na.omit`

. Another possible value is`NULL`

, no action. Value`na.exclude`

can be useful.`model`

a logical value indicating whether

*model frame*should be included as a component of the returned value.`x`

,`y`

logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value.

`contrasts`

an optional list. See the

`contrasts.arg`

of`model.matrix.default`

.`control`

a list of parameters for the fitting process. See

`mclogit.control`

`...`

arguments to be passed to

`mclogit.control`

## Value¶

`mblogit`

returns an object of class “mblogit”, which has almost the
same structure as an object of class “glm”. The difference are the
components `coefficients`

, `residuals`

, `fitted.values`

,
`linear.predictors`

, and `y`

, which are matrices with number of
columns equal to the number of response categories minus one.

## Details¶

The function `mblogit`

internally rearranges the data into a ‘long’
format and uses `mclogit.fit`

to compute estimates. Nevertheless, the
‘user data’ is unaffected.