R help - Jun 2013 - Problem with marginal effects of a multinomial logistic regression

Hi r-users,

I try to calculate marginal effects of a multinomial logistic regression. To do
this i use mlogit package and effects() function.

Here is how the procedure works (source : effects() function of mlogit package)
:
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)
I have no problem with first step (mlogit.data() function). I think my problem
is on the specification of the multinomial regression.

My regression (for example with three variables) is on the form: Y ~ 0 | X1 + X2
+ X3. When I try to estimate the marginal effects for model 2 variables no
problem, however for 3 variables R console returns me the following error:
"Error in if (rhs% in% c (1, 3)) {: argument is of length zero "
(translation from error in R console in french).

To understand what is my problem I tried to perform a multinomial regression of
similar shape on the dataset "Fishing", ie: mode ~ 0 | income + price
+ catch (even if this form has no "economic" sense.) Again the R
console returns me the same error for 3 variables but manages to estimate these
effects for a model with two variables ...

This leads me to think that my problem really comes from the specification of my
multinomial regression ... Do you know how I could find a solution to my
problem?

Thank you for your help :)

Léonard.
	[[alternative HTML version deleted]]

Hi r-users,

I try to calculate marginal effects of a multinomial logistic regression. To do
this i use mlogit package and effects() function.

Here is how the procedure works (source : effects() function of mlogit package)
:
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)
I have no problem with first step (mlogit.data() function). I think my problem
is on the specification of the multinomial regression.

My regression (for example with three variables) is on the form: Y ~ 0 | X1 + X2
+ X3. When I try to estimate the marginal effects for model 2 variables no
problem, however for 3 variables R console returns me the following error:
"Error in if (rhs% in% c (1, 3)) {: argument is of length zero "
(translation from error in R console in french).

To understand what is my problem I tried to perform a multinomial regression of
similar shape on the dataset "Fishing", ie: mode ~ 0 | income + price
+ catch (even if this form has no "economic" sense.) Again the R
console returns me the same error for 3 variables but manages to estimate these
effects for a model with two variables ...

This leads me to think that my problem really comes from the specification of my
multinomial regression ... Do you know how I could find a solution to my
problem?

Thank you for your help :)

Léonard.
	[[alternative HTML version deleted]]