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Last data update: 2014.03.03

R: Marginal effects of the covariates

effects.mlogit

R Documentation

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 class 'mlogit'
effects(object, covariate = NULL,
                        type = c("aa", "ar", "rr", "ra"), data = NULL, ...)

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.

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.

Author(s)

Yves Croissant

Examples

data("Fishing", package = "mlogit")
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, index(m)$alt, mean),
                           catch = tapply(catch, index(m)$alt, mean),
                           income = mean(income)))
# compute the marginal effects (the second one is an elasticity
effects(m, covariate = "income", data = z)
effects(m, covariate = "price", type = "rr", data = z)
effects(m, covariate = "catch", type = "ar", data = z)