On Aug 9, 2013, at 6:44 AM, Kevin Shaney wrote:
>
> Hello -
>
> I have been using GLMNET of the following form to predict multinomial
> logistic / class dependent variables:
>
> mglmnet=glmnet(xxb,yb ,alpha=ty,dfmax=dfm,
> family="multinomial",standardize=FALSE)
>
> I am using both continuous and categorical variables as predictors, and am
> using sparse.model.matrix to code my x's into a matrix. This is changing an
> example categorical variable whose original name / values is {V1 = "1" or "2"
> or "3"} into two recoded variables {V12= "1" or "0" and V13 = "1" or "0"}.
You set their penalty factors to be 0 to at least observe the case where
inclusion is performed. And setting the penallty factor for both to be small
would allow you to "honestly" use 0 as the estimated coefficient in such cases
where one was estimated and the other not.
>
> As i am cycling through different penalties, i would like to either have both
> recoded variables included or both excluded, but not one included - and
> can't figure out how to make that work. I tried changing the
> "type.multinomial" option, as that looks like this option should do what i
> want, but can't get it to work (maybe the difference in recoded variable
> names is driving this).
Doesn't the 'family' argument, used to set what I think you are calling 'type',
just refer to the y argument, rather than the predictors. You may want:
mglmnet=glmnet(xxb,yb ,alpha=ty,dfmax=dfm, type.multinomial="grouped",
family="multinomial",standardize=FALSE)
>
> To summarize, for categorical variables, i would like to hierarchically
> constrain inclusion / exclusion of recoded variables in the model - either
> all of the recoded variables from the same original categorical variable are
> in, or all are out.
I do understand that I am possibly not directly answering your question, but in
some respect I wonder if it deserves an answer. I think it is meaningful if
some factor levels are "penalized-out" of models.
--
David Winsemius
Alameda, CA, USA
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