Xiaoqi, You need to specify the sizes. There are other search algorithms that auotmatically pick the size (such as genetic algorithms), but I don't have those in the package yet.
Another approach is to use univariate filtering (see the sbf function in caret). Max On Mar 13, 2011, at 8:49 PM, Xiaoqi Cui <x...@mtu.edu> wrote: > Thanks for your prompt reply! > > You're right, I didn't add the parameter "importance=TRUE" when I used > function "train" to fit the random forest model. Once I used the above > parameter, everything went well. Also the functions "varImp" and "plot" work > well too. > > I noticed "caret" is really good at selecting important predictors. Here I > just have another question about using the package "caret" to select the best > subset of predictors. As I know, the function "rfe" can be used to select the > optimal set of important predictors given a series of sizes of the subsets. > I'm wondering if "caret" can automatically give the best size of the selected > subset without user providing the candidate sizes. Thanks, > > Best, > > Xiaoqi > ----- Original Message ----- > From: "Max Kuhn" <mxk...@gmail.com> > To: "Xiaoqi Cui" <x...@mtu.edu> > Cc: r-help@r-project.org > Sent: Monday, March 7, 2011 2:33:06 PM GMT -06:00 US/Canada Central > Subject: Re: [R] use "caret" to rank predictors by random forest model > > It would help if you provided the code that you used for the caret functions. > > The most likely issues is not using importance = TRUE in the call to train() > > I believe that I've only implemented code for plotting the varImp > objects resulting from train() (eg. there is plot.varImp.train but not > plot.varImp). > > Max > > On Mon, Mar 7, 2011 at 3:27 PM, Xiaoqi Cui <x...@mtu.edu> wrote: >> Hi, >> >> I'm using package "caret" to rank predictors using random forest model and >> draw predictors importance plot. I used below commands: >> >> rf.fit<-randomForest(x,y,ntree=500,importance=TRUE) >> ## "x" is matrix whose columns are predictors, "y" is a binary resonse vector >> ## Then I got the ranked predictors by ranking >> "rf1$importance[,"MeanDecreaseAccuracy"]" >> ## Then draw the importance plot >> varImpPlot(rf.fit) >> >> As you can see, all the functions I used are directly from the package >> "randomForest", instead of from "caret". so I'm wondering if the package >> "caret" has some functions who can do the above ranking and ploting. >> >> In fact, I tried functions "train", "varImp" and "plot" from package >> "caret", the random forest model that built by "train" can not be input >> correctly to "varImp", which gave error message like "subscripts out of >> bounds". Also function "plot" doesn't work neither. >> >> So I'm wondering if anybody has encountered the same problem before, and >> could shed some light on this. I would really appreciate your help. >> >> Thanks, >> Xiaoqi >> >> ______________________________________________ >> 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.
