Hi,
On Fri, Aug 9, 2013 at 6:44 AM, Kevin Shaney <kevin.sha...@rosetta.com> 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"}.
>
> 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).
>
> 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.
Pretty sure that you'll need the "grouped lasso" for that. Quick
googling over CRAN suggests:
grplasso: http://cran.r-project.org/web/packages/grplasso/index.html
standGL: http://cran.r-project.org/web/packages/standGL/index.html
gglasso: http://code.google.com/p/gglasso/
Unfortunately it doesn't look like any of them support the equivalent
of family="multinomial", only 2-class classification.
HTH,
-steve
--
Steve Lianoglou
Computational Biologist
Bioinformatics and Computational Biology
Genentech
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