rrfcv: Random Forest Cross-Valdidation for feature selection

In RRF: Regularized Random Forest

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

rrfcv(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 RRF call
recursive	whether variable importance is (re-)assessed at each step of variable reduction
...	other arguments passed on to RRF

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.

See Also

RRF, importance

Examples

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


set.seed(647)
myiris <- cbind(iris[1:4], matrix(runif(508 * nrow(iris)), nrow(iris), 508))
result <- rrfcv(myiris, iris$Species)
with(result, plot(n.var, error.cv, log="x", type="o", lwd=2))

result <- replicate(5, rrfcv(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")

RRF documentation built on May 31, 2022, 1:05 a.m.