[R] use "caret" to rank predictors by random forest model

[Max Kuhn]

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 <xcui at 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
>
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-- 

Max