AIC.gmnl: Akaike's Information Criterion

In gmnl: Multinomial Logit Models with Random Parameters

Description

Calculate the Akaike's information Criterion (AIC) or the Bayesian information Criterion (BIC) for an object of class gmnl.

Usage

## S3 method for class 'gmnl'
AIC(object, ..., k = 2)

## S3 method for class 'gmnl'
BIC(object, ...)

Arguments

object	a fitted model of class gmnl.
...	additional arguments to be passed to or from other functions.
k	a numeric value, use as penalty coefficient for number of parameters in the fitted model.

Details

For more information see AIC or BIC

Value

A numeric value with the corresponding AIC or BIC value.

See Also

gmnl for the estimation of multinomial logit models with observed and unobserved individual heterogeneity.

Examples

## Estimate MNL model
data("TravelMode", package = "AER")
library(mlogit)
TM <- mlogit.data(TravelMode, choice = "choice", shape = "long", 
                 alt.levels = c("air", "train", "bus", "car"), chid.var = "individual")
                 
mnl <- gmnl(choice ~ wait + vcost + travel + gcost | 0 , data = TM)
AIC(mnl)
BIC(mnl)

gmnl documentation built on July 1, 2020, 6:01 p.m.