randomForest: rfcv – R documentation

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rfcv

Random Forest Cross-Valdidation for feature selection

Description

This function shows the cross-validated prediction performance of models with sequentially reduced number of predictors (ranked by variable importance) via a nested cross-validation procedure.

Usage

rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5,
     mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...)

Arguments

`trainx`	matrix or data frame containing columns of predictor variables
`trainy`	vector of response, must have length equal to the number of rows in `trainx`
`cv.fold`	number of folds in the cross-validation
`scale`	if `"log"`, reduce a fixed proportion (`step`) of variables at each step, otherwise reduce `step` variables at a time
`step`	if `log=TRUE`, the fraction of variables to remove at each step, else remove this many variables at a time
`mtry`	a function of number of remaining predictor variables to use as the `mtry` parameter in the `randomForest` call
`recursive`	whether variable importance is (re-)assessed at each step of variable reduction
`...`	other arguments passed on to `randomForest`

Value

A list with the following components:

list(n.var=n.var, error.cv=error.cv, predicted=cv.pred)

`n.var`	vector of number of variables used at each step
`error.cv`	corresponding vector of error rates or MSEs at each step
`predicted`	list of `n.var` components, each containing the predicted values from the cross-validation

Author(s)

Andy Liaw

References

Svetnik, V., Liaw, A., Tong, C. and Wang, T., “Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules”, MCS 2004, Roli, F. and Windeatt, T. (Eds.) pp. 334-343.

Examples

set.seed(647)
myiris <- cbind(iris[1:4], matrix(runif(96 * nrow(iris)), nrow(iris), 96))
result <- rfcv(myiris, iris$Species, cv.fold=3)
with(result, plot(n.var, error.cv, log="x", type="o", lwd=2))

## The following can take a while to run, so if you really want to try
## it, copy and paste the code into R.

## Not run: 
result <- replicate(5, rfcv(myiris, iris$Species), simplify=FALSE)
error.cv <- sapply(result, "[[", "error.cv")
matplot(result[[1]]$n.var, cbind(rowMeans(error.cv), error.cv), type="l",
        lwd=c(2, rep(1, ncol(error.cv))), col=1, lty=1, log="x",
        xlab="Number of variables", ylab="CV Error")

## End(Not run)

randomForest

Breiman and Cutler's Random Forests for Classification and Regression

v4.6-14

GPL (>= 2)

Authors

Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener.

Initial release

2018-03-22