mlogit: 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.

Value

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.

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.

Examples

Run this code

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