Description Usage Arguments Details Value Author(s) References See Also Examples
Random Forest CrossValdidation for feature selection
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trainx 
matrix or data frame containing columns of predictor variables 
trainy 
vector of response, must have length equal to the number of rows in

cv.fold 
The fold, the defalut is 5. 
scale 
If 
step 
If 
mtry 
A function of number of remaining predictor variables to use as the

recursive 
Whether variable importance is (re)assessed at each step of variable reduction 
This function shows the crossvalidated prediction performance of models with sequentially reduced number of predictors (ranked by variable importance) via a nested crossvalidation procedure.
A list with the following three components::
n.var
 vector of number of variables used at each step
error.cv
 corresponding vector of error rates or MSEs at each step
res
 list of n.var components, each containing the feature importance values from
the crossvalidation
Minfeng Zhu <wind2zhu@163.com>
Svetnik, V., Liaw, A., Tong, C. and Wang, T., Application of Breiman's Random Forest to Modeling StructureActivity Relationships of Pharmaceutical Molecules, MCS 2004, Roli, F. and Windeatt, T. (Eds.) pp. 334343.
See rf.cv
for the CrossValidation of Classification and
Regression models using Random Forest
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