Thanks! I tried doing the type.multinomial="grouped" argument - but it didn't work for me. Maybe I did something wrong. I thought I understood why it didn't work because of sparse.model.matrix recoding variables (like below to V12 & V13} makes GLMNET unable to tell that they actually came from the same source categorical variable. Has that option ever worked for you in a similar situation?
Thanks! Kevin From: David Winsemius [via R] [mailto:ml-node+s789695n4673463...@n4.nabble.com] Sent: Friday, August 09, 2013 3:14 PM To: Kevin Shaney Subject: Re: glmnet inclusion / exclusion of categorical variables 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 ______________________________________________ [hidden email]</user/SendEmail.jtp?type=node&node=4673463&i=0> mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ________________________________ If you reply to this email, your message will be added to the discussion below: http://r.789695.n4.nabble.com/glmnet-inclusion-exclusion-of-categorical-variables-tp4673400p4673463.html To unsubscribe from glmnet inclusion / exclusion of categorical variables, click here<http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by_code&node=4673400&code=a2V2aW4uc2hhbmV5QHJvc2V0dGEuY29tfDQ2NzM0MDB8MTI2ODM3OTQw>. NAML<http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=macro_viewer&id=instant_html%21nabble%3Aemail.naml&base=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace&breadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> ________________________________ This e-mail message contains information that may be non...{{dropped:13}} ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
