effects.mlogit {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 |
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
|
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 matrix is
returned,
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
is returned.
Author(s)
Yves Croissant
See Also
mlogit()
for the estimation of multinomial logit
models.
Examples
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)