The three tests for mlogit models

Description

Three tests for mlogit models: specific methods for the Wald test and the likelihood ration test and a new function for the score test

Usage

scoretest(object, ...)
## S3 method for class 'mlogit'
scoretest(object, ...)
## S3 method for class 'mlogit'
waldtest(object, ...)
## S3 method for class 'mlogit'
lrtest(object, ...)

Arguments

Details

The "scoretest" function and "mlogit" method for "waldtest" and "lrtest" from the "lmtest" package provides the infrastructure to compute the three tests of hypothesis for "mlogit" objects.

The first argument must be a "mlogit" object. If the second one is a fitted model or a formula, the behaviour of the three functions is the one of the default methods of "waldtest" and "lrtest": the two models provided should be nested and the hypothesis tested is that the constrained model is the ‘right’ model.

If no second model is provided and if the model provided is the constrained model, some specific arguments of "mlogit" should be provided to descibe how the initial model should be updated. If the first model is the unconstrained model, it is tested versus the ‘natural’ constrained model; for example, if the model is a heteroscedastic logit model, the constrained one is the multinomial logit model.

Value

an object of class "htest".

Author(s)

Yves Croissant

Examples

library("mlogit")
data("TravelMode", package = "AER")
ml <- mlogit(choice ~ wait + travel + vcost, TravelMode,
             shape = "long", chid.var = "individual", alt.var = "mode")
hl <- mlogit(choice ~ wait + travel + vcost, TravelMode,
             shape = "long", chid.var = "individual", alt.var = "mode",
             method = "bfgs", heterosc = TRUE)
lrtest(ml, hl)
waldtest(hl)
scoretest(ml, heterosc = TRUE)

Results