Hi Mark, To put some context to David's response below, you can search the list archives for times when people ask about stepwise regression. You can get started here:
http://search.gmane.org/search.php?group=gmane.comp.lang.r.general&query=stepwise+penalized The long and short of it is that you are almost always encouraged to use some regularization/penalized model instead of this stepwise approach. Frank Harrell, in particular, is generally quite vocal against stepwise regression -- I'm actually surprised he hasn't chimed in by now, but maybe he's getting a bit tired of fighting the good fight -- or, it's close to the holiday and he's taking a break ;-) Anyway ... HTH, -steve On Fri, Nov 16, 2012 at 4:13 PM, David Winsemius <[email protected]> wrote: > > On Nov 16, 2012, at 12:16 PM, Mark Ebbert wrote: > >> I haven't heard anything on this question. Is there something fundamentally >> wrong with my question? Any feedback is appreciated. >> > > Perhaps failure to read this sig at the bottom of every posted message to > rhelp? > > "PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code." > > >> Mark >> On Nov 15, 2012, at 8:13 AM, Mark T. W. Ebbert wrote: >> >>> Dear Gurus, >>> >>> Thank you in advance for your assistance. I'm trying to understand scope >>> better when performing stepwise regression using "step." > > From the help page of step: > "If scope is a single formula, it specifies the upper component, and the > lower model is empty. " > >>> I have a model with a binary response variable and 10 predictor variables. >>> When I perform stepwise regression I define scope=.^2 to allow interactions >>> between all terms. > > I generally avoid answering questions about stepwise regression, because most > of them do not include sufficient background material to justify that > strategy. Yours certainly did not. > > >>> But I am missing something. When I perform stepwise regression (both >>> directions) on the main model (y~x1+x2+…+x10) the method returns quickly >>> with an answer; however, when I define all interactions in the main model >>> (y~x1+x2+…+x10+x1:x2+x1:x3+…) and then perform stepwise regression >>> (backward only) it runs so long I have to kill it. >>> >>> So here's my question: what is the difference between scope=.^2 on the >>> additive (proper term?) model and defining all interactions and doing >>> backward regression? My understanding is that .^2 is supposed to allow all >>> interactions! > > Well, I would have guessed all two-way interactions (all 45 of them in your > case) would be included and then successively reduce until you got to your > specified (arbitrary and most likely incorrectly set) endpoint.) I think the > help page Details section is unclear on this point. I do not think that the > 120 potential three-way interactions are part of the scope in that instance, > but it should be easy enough for you to test that possibility. > > -- > David Winsemius, MD > Alameda, CA, USA > > ______________________________________________ > [email protected] 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. -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact ______________________________________________ [email protected] 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.
