Hi, On Sun, Jun 5, 2011 at 9:12 PM, Dae-Jin Lee <lee.dae...@gmail.com> wrote: > Dear R-users > > I'm trying to use lasso in lars package for subset regression, I have a > large matrix of size 1000x100 and my aim is to select a subset k of the 100 > variables. > > Is there any way in lars to fix the number k (i.e. to select the best 10 > variables) > > library(lars) > > aa=lars(X,Y,type="lasso",max.steps=200) > > plot(aa,plottype="Cp") > aa$RSS > which.min(aa$RSS) > round(aa$beta,2) > > aa$beta[which.min(aa$RSS),] # find which coefficients minimizes the RSS > > lasso.ind=which((as.vector((aa$beta[which.min(aa$RSS),])))>0) # index of > variables > > print(lasso.ind) # this usually gives more than 10 variables (also depends > on the max.steps in lars)
First off: I'd suggest using the glmnet package instead of lars. Setting its `alpha` parameter to 1 will give you the lasso, but you can also play w/ different values of alpha to see if an elasticnet-type penalty would be better. Now that you are using glmnet, check its `dfmax` and `pmax` arguments. HTH, -steve -- 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 ______________________________________________ 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.
