Hola! If the original questioner wants a guide as to how variables to measure IN THE FUTURE, when using his model in practice, thwen I think he will be unhappy with any advice which forces him to measure each of the 44 variables when probably a small subset will do! What is wrong with first using, let us say, penalized likelihood, maybe with CV to choose degree of smoothing, and SECONDLY using stepwise (maybee stepAIC from MASS) with the predicted values from the first step model to get a good few-vatiables approximation which can be used in practice? If my memory is'nt too bad, that idea is from harrel's book.
Kjetil On Mon, Sep 29, 2008 at 9:50 PM, Frank E Harrell Jr < [EMAIL PROTECTED]> wrote: > Greg Snow wrote: > >> -----Original Message----- >>> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] >>> project.org] On Behalf Of Frank E Harrell Jr >>> Sent: Saturday, September 27, 2008 7:15 PM >>> To: Darin Brooks >>> Cc: [EMAIL PROTECTED]; [EMAIL PROTECTED]; >>> [EMAIL PROTECTED] >>> Subject: Re: [R] FW: logistic regression >>> >>> Darin Brooks wrote: >>> >>>> Glad you were amused. >>>> >>>> I assume that "booking this as a fortune" means that this was an >>>> >>> idiotic way >>> >>>> to model the data? >>>> >>> Dieter was nominating this for the "fortunes" package in R. (Thanks >>> Dieter) >>> >>> MARS? Boosted Regression Trees? Any of these a better choice to >>>> >>> extract >>> >>>> significant predictors (from a list of about 44) for a measured >>>> >>> dependent >>> >>>> variable? >>>> >>> Or use a data reduction method (principal components, variable >>> clustering, etc.) or redundancy analysis (to remove individual >>> predictors before examining associations with Y), or fit the full model >>> using penalized maximum likelihood estimation. lasso and lasso-like >>> methods are also worth pursuing. >>> >> >> Frank (and any others who want to share an opinion): >> >> What are your thoughts on model averaging as part of the above list? >> > > Model averaging has good performance but no advantage over fitting a single > complex model using penalized maximum likelihood estimation. > > Frank > > >> >> -- >> Gregory (Greg) L. Snow Ph.D. >> Statistical Data Center >> Intermountain Healthcare >> [EMAIL PROTECTED] >> 801.408.8111 >> >> >> >> > > -- > Frank E Harrell Jr Professor and Chair School of Medicine > Department of Biostatistics Vanderbilt University > > ______________________________________________ > 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. > [[alternative HTML version deleted]] ______________________________________________ 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.
