There is also a sparse PLS model in the spls package. It uses lasso-like regularization to reduce the number of variables. I've had a lot of success with it.
Max 2009/11/5 Ricardo Gonçalves Silva <ricard...@terra.com.br>: > Hi Guys, > > Of course, a backward, forward, or other methods can be used directly. But > concerning BMA, the model interpretation is far simple: > > "Bayesian Model Averaging accounts for the model uncertainty inherent in the > variable selection problem by averaging over the best models in the model > class according to approximate posterior model probability." > > If you want to learn a few more before continue, that a look at the BMA > homepage: > > http://www2.research.att.com/~volinsky/bma.html > > But of course, you must do what you think is better for your problem. > By the way what is the dimension of your problem? > > HTH, > > Rick > -------------------------------------------------- > From: "Frank E Harrell Jr" <f.harr...@vanderbilt.edu> > Sent: Thursday, November 05, 2009 4:12 PM > To: "Ricardo Gonçalves Silva" <ricard...@terra.com.br> > Cc: "bbslover" <dlu...@yeah.net>; <r-help@r-project.org> > Subject: Re: [R] variable selectin---reduce the numbers of initial variable > >> Ricardo Gonçalves Silva wrote: >>> >>> Yes, right. But I still prefer using BMA. >>> Best, >>> >>> Rick >> >> If you are entertaining only one model family, them BMA is a long, >> tedious, complex way to obtain shrinkage and the resulting averaged >> model is very difficult to interpret. Consider a more direct approach. >> >> Frank >> >>> >>> -------------------------------------------------- >>> From: "bbslover" <dlu...@yeah.net> >>> Sent: Wednesday, November 04, 2009 11:28 PM >>> To: <r-help@r-project.org> >>> Subject: Re: [R] variable selectin---reduce the numbers of initial >>> variable >>> >>>> >>>> thank you . I can try bayesian. PCA method that I used to is can get >>>> some >>>> pcs, but I donot know how can i use the original variables in that >>>> equation, >>>> maybe I should select those have high weight ones,and delete that less >>>> weight ones. right? >>>> >>>> Ricardo Gonçalves Silva wrote: >>>>> >>>>> Hi, >>>>> >>>>> Nowdays there's a lot o new variable selection methods, specially using >>>>> the >>>>> Bayes Paradigm. >>>>> For your problem, I think you could try the Bayesian Model Average BMA >>>>> package. >>>>> Or, you can reduce your data dimension by PCA, which also permits you >>>>> see >>>>> the weight of >>>>> each variable in the PC. >>>>> >>>>> HTH >>>>> >>>>> Rick >>>>> >>>>> -------------------------------------------------- >>>>> From: "bbslover" <dlu...@yeah.net> >>>>> Sent: Wednesday, November 04, 2009 10:23 AM >>>>> To: <r-help@r-project.org> >>>>> Subject: [R] variable selectin---reduce the numbers of initial >>>>> variable >>>>> >>>>>> >>>>>> hello, >>>>>> >>>>>> my problem is like this: now after processing the varibles, the >>>>>> remaining >>>>>> 160 varibles(independent) and a dependent y. when I used PLS method, >>>>>> with >>>>>> 10 >>>>>> components, the good r2 can be obtained. but I donot know how can I >>>>>> express >>>>>> my equation with the less varibles and the y. It is better to use less >>>>>> indepent varibles. that is how can I select my indepent varibles. >>>>>> Maybe >>>>>> GA is good method, but now I donot gasp it. and can you give me more >>>>>> good >>>>>> varibles selection's methods. and In R, which method can be used to >>>>>> select >>>>>> the potent varibles . and using the selected varibles to model a >>>>>> equation >>>>>> with higher r2, q2,and less RMSP. >>>>>> >>>>>> thank you! >>>>>> -- >>>>>> View this message in context: >>>>>> >>>>>> http://old.nabble.com/variable-selectin---reduce-the-numbers-of-initial-variable-tp26195345p26195345.html >>>>>> >>>>>> Sent from the R help mailing list archive at Nabble.com. >>>>>> >>>>>> ______________________________________________ >>>>>> 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. >>>>>> >>>>> >>>>> >>>>> >>>>>> >>>>>> No virus found in this incoming message. >>>>>> Checked by AVG - www.avg.com >>>>>> Version: 9.0.698 / Virus Database: 270.14.48/2479 - Release Date: >>>>>> 11/03/09 >>>>>> 17:38:00 >>>>>> >>>>> >>>>> ______________________________________________ >>>>> 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. >>>>> >>>>> >>>> >>>> -- >>>> View this message in context: >>>> >>>> http://old.nabble.com/variable-selectin---reduce-the-numbers-of-initial-variable-tp26195345p26207750.html >>>> >>>> Sent from the R help mailing list archive at Nabble.com. >>>> >>>> __________________ >> >> -- >> Frank E Harrell Jr Professor and Chair School of Medicine >> Department of Biostatistics Vanderbilt University >> > > > >> >> No virus found in this incoming message. >> Checked by AVG - www.avg.com >> Version: 9.0.698 / Virus Database: 270.14.49/2480 - Release Date: 11/04/09 >> 05:37:00 >> > > ______________________________________________ > 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. > -- Max ______________________________________________ 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.
