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.
Thanks!
Kevin
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