mlogit {globaltest} | R Documentation

## Multinomial Logistic Regression

### Description

Fits a multinomial logistic regression model to a nominal scale outcome.

### Usage

    mlogit(formula, data, control = glm.control())

### Arguments

`formula` | An object of class `formula` containing a symbolic description of the model to be fit. See the documentation of `formula` for details.
`data` | An optional data frame containing the variables in the model. If not found in 'data', the variables are taken from the environment from which 'mlogit' is called.
`control` | A list of parameters for controlling the fitting process. See the documentation of `glm.control` for details.

### Details

The function mlogit fits a multinomial logistic regression model for a multi-valued outcome with nominal scale. The implementation and behaviour are designed to mimic those of `glm`, but the options are (as yet) more limited. Missing values are not allowed in the data.

The model is fitted without using a reference outcome category; the parameters are made identifiable by the requirement that the sum of corresponding regression coefficients over the outcome categories is zero.

### Value

An object of (S4) class `mlogit`. The class has slots: coefficients (matrix), standard.err (matrix), fitted.values (matrix), x (matrix), y (matrix), formula (formula), call (call), df.null (numeric), df.residual (numeric), null.deviance (numeric), deviance (numeric), iter (numeric), converged (logical).

Methods implemented for the `mlogit` class are `coefficients`, `fitted.values`, `residuals` and which extract the relevant quantities, and `summary`, which gives the same output as with a `glm` object.

### Author(s)

Jelle Goeman: j.j.goeman@lumc.nl; Jan Oosting

### See Also

`glm`, `multinom`.

### Examples

      y <- factor(rep(1:4, 5))
      x <- 1:20
      fit <- mlogit(y ~ x)
      summary(fit)
      residuals(fit)

[Package globaltest version 5.46.0 Index]