mlogit and model-based recursive partitioning

Tudor:
Has anyone tried to model-based recursive partition (using mob from 
package party; thanks Achim and colleagues) a data set based on a 
multinomial logit model (using mlogit from package mlogit; thanks Yves)?
Interesting question: in principle, this is possible but I wouldn't know 
of anyone who has tried this.
I attempted to do so, but there are at least two reasons why I could 
not. First, in mob I am not quite sure that a model of class StatModel 
exists for mlogit models.  Second, as mlogit uses the pipe character | 
to specify the model, I wonder how this would interact with mob which 
uses pipe to differentiate between explanatory and segmentation 
variables.
This is one but not the only complication when trying to actually combine 
mlogit and mob. I think the building blocks would have to be:

- Set up the data plus formula handling. As you point out, that would need 
a three-part formula separating alternative-specific and subject-specific 
regressors and partitioning variables. Furthermore you would probably need 
to translate between the long format used by mlogit (subjects x 
alternatives) to the wide format because mob would want to partition the 
subjects.

- A StatModel object would be required. Personally, if I wanted to do it, 
would try to set up the StatModel object on the fly (rather than predefine 
it in a package) so that the StatModel creator can depend on the 
formula/data. The formula/data processing described above can be done 
inside the StatModel object.

- Finally, the required methods for the fitted model object would have to 
be defined. In particular, the subject-specific gradients would be 
required. I think currently, mlogit just provides the overall gradient.

So, in summary: It can be done but it would likely need more than just an 
hour of coding...

hth,
Z
An example (not working) of what I would like to accomplish follows below.

Thanks a lot.

Tudor

library(party)
library(mlogit)
data("Fishing", package = "mlogit")
Fish <- mlogit.data(Fishing, varying = c(2:9), shape = "wide", choice =
"mode")
# FIT AN mlogit MODEL
m1 <- mlogit(mode ~ price + catch, data=Fish)
# THE DESIRED END RESULT:  SEGMENT m1 BASED ON INCOME AND/OR OTHER POSSIBLE
COVARIATES

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mlogit and model-based recursive partitioning

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