effects.mlogit: Marginal effects of the covariates

Description

The effects method for mlogit objects computes the marginal effects of the selected covariate on the probabilities of choosing the alternatives

Usage

# S3 method for mlogit
effects(
  object,
  covariate = NULL,
  type = c("aa", "ar", "rr", "ra"),
  data = NULL,
  ...
)

Value

If the covariate is alternative specific, a \(J \times J\) matrix is returned, \(J\) being the number of alternatives. Each line contains the marginal effects of the covariate of one alternative on the probability to choose any alternative. If the covariate is individual specific, a vector of length \(J\) is returned.

Arguments

object: a mlogit object,
covariate: the name of the covariate for which the effect should be computed,
type: the effect is a ratio of two marginal variations of the probability and of the covariate ; these variations can be absolute "a" or relative "r". This argument is a string that contains two letters, the first refers to the probability, the second to the covariate,
data: a data.frame containing the values for which the effects should be calculated. The number of lines of this data.frame should be equal to the number of alternatives,
...: further arguments.

Author

Yves Croissant

Examples

Run this code


data("Fishing", package = "mlogit")
library("zoo")
Fish <- mlogit.data(Fishing, varying = c(2:9), shape = "wide", choice = "mode")
m <- mlogit(mode ~ price | income | catch, data = Fish)
# compute a data.frame containing the mean value of the covariates in
# the sample
z <- with(Fish, data.frame(price = tapply(price, idx(m, 2), mean),
                           catch = tapply(catch, idx(m, 2), mean),
                           income = mean(income)))
# compute the marginal effects (the second one is an elasticity
## IGNORE_RDIFF_BEGIN
effects(m, covariate = "income", data = z)
## IGNORE_RDIFF_END
effects(m, covariate = "price", type = "rr", data = z)
effects(m, covariate = "catch", type = "ar", data = z)