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.