I would like to use rfcv to cull the unimportant variables from a data set before creating a final random forest with more trees (please correct and inform me if that's not the way to use this function). For example,
> data(fgl, package="MASS")
> tst <- rfcv(trainx = fgl[,-10], trainy = fgl[,10], scale = "log", step=0.7)
> tst$error.cv
9 6 4 3 2 1
0.2289720 0.2149533 0.2523364 0.2570093 0.3411215 0.5093458
In this case, if I understand the result correctly, it seems that we can remove three variables without negative side effects. However,
> attributes(tst)
$names
[1] "n.var" "error.cv" "predicted"
None of these slots tells me what those first three variables that can be harmlessly removed from the dataset actually were.