The effects
method for mlogit
objects computes the
marginal effects of the selected covariate on the probabilities of
choosing the alternatives
1 2 3 
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.
1 2 3 4 5 6 7 8 9 10 11 12  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)

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