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)

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning