[R] multinomial: stepwise selection using prediction error

[rdiaz]

Dear All,

I often come across observational data sets, where the interest is in predicting
the class membership (often more than 2 classes) as a function of several
variables; generally, the number of predictors is very large, and it is also of
interest to make that number as small as possible (for instance, to minimize length
of future questionaires). I thought that a possible approach would be to use some
kind of stepwise model selection; as criterion for variable selection I would use
the prediction error from models fitted with "multinom" (package nnet), where the
prediction error would be obtained using k-fold cross-validation.

I have seen somewhat similar approaches, but not this one in particular, and since
I'd think the general situation is fairly common to many people,  I am wondering
whether the idea makes sense, or if it is a completely misguided and boneheaded
one.

(I think this is relatively easy to implement, comparing the results of
predict.multinom with the true class membership of the hold-out sets; that would be
the value returned by the function "extractAIC.mycvmultinom", and then I would be
able to just call stepAIC on objects of class "mycvmultinom").

Thanks,

Ramón Díaz
Inner Research
Velázquez 109
28006 Madrid
Spain


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