rfcv
Random Forest Cross-Valdidation for feature selection
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.
- Keywords
- regression, classif
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 reducestepvariables 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
mtryparameter in therandomForestcall- 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)
vector of number of variables used at each step
corresponding vector of error rates or MSEs at each step
list of n.var components, each containing
the predicted values from the cross-validation
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
Examples
# NOT RUN {
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")
# }