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

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