effects.mlogit {mlogit} | R Documentation |
The effects
method for mlogit
objects computes the
marginal effects of the selected covariate on the probabilities of
choosing the alternatives
## S3 method for class 'mlogit' effects(object, covariate = NULL, type = c("aa", "ar", "rr", "ra"), data = NULL, ...)
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. |
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.
Yves Croissant
mlogit
for the estimation of multinomial logit models.
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)