#' rfcv
#' @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, ...)
#' @param trainx, matrix or data frame containing columns of predictor variables
#' @param trainoffset, vector of offset, must have length equal to the number of rows in \code{trainx}
#' @param trainy, vector of response, must have length equal to the number of rows in \code{trainx}
#' @param cv.fold, number of folds in the cross-validation
#' @param scale, if \code{"log"}, reduce a fixed proportion (\code{step}) of variables at each step, otherwise reduce step variables at a time
#' @param step, if \code{log=TRUE}, the fraction of variables to remove at each \code{step}, else remove this many variables at a time
#' @param mtry, a function of number of remaining predictor variables to use as the \code{mtry} parameter in the \code{rfPoisson} call
#' @param recursive, whether variable importance is (re-)assessed at each step of variable reduction
#' @param ..., other arguments passed on to \code{rfPoisson}
#' @author 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.
#' @export
rfcv <- function(trainx, trainoffset, trainy, cv.fold=5, scale="log", step=0.5,
mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE,
...) {
n <- nrow(trainx)
p <- ncol(trainx)
if (scale == "log") {
k <- floor(log(p, base=1/step))
n.var <- round(p * step^(0:(k-1)))
same <- diff(n.var) == 0
if (any(same)) n.var <- n.var[-which(same)]
if (! 1 %in% n.var) n.var <- c(n.var, 1)
} else {
n.var <- seq(from=p, to=1, by=step)
}
k <- length(n.var)
cv.pred <- vector(k, mode="list")
for (i in 1:k) cv.pred[[i]] <- trainy
## Generate the indices of the splits
## For regression, bin the response into 5 bins and stratify.
f <- factor(rep(1:5, length=length(trainy))[order(order(trainy))])
nlvl <- table(f)
idx <- numeric(n)
for (i in 1:length(nlvl)) {
idx[which(f == levels(f)[i])] <- sample(rep(1:cv.fold, length=nlvl[i]))
}
for (i in 1:cv.fold) {
## cat(".")
all.rf <- rfPoisson(trainx[idx != i, , drop=FALSE],
trainoffset[idx != i],
trainy[idx != i],
trainx[idx == i, , drop=FALSE],
trainy[idx == i],
trainoffset[idx == i],
mtry=mtry(p), importance=TRUE, ...)
cv.pred[[1]][idx == i] <- all.rf$test$predicted
impvar <- (1:p)[order(all.rf$importance[,1], decreasing=TRUE)]
for (j in 2:k) {
imp.idx <- impvar[1:n.var[j]]
sub.rf <-
rfPoisson(trainx[idx != i, imp.idx, drop=FALSE],
trainoffset[idx != i],
trainy[idx != i],
trainx[idx == i, imp.idx, drop=FALSE],
trainy[idx == i],
trainoffset[idx == i],
mtry=mtry(n.var[j]), importance=recursive, ...)
cv.pred[[j]][idx == i] <- sub.rf$test$predicted
## For recursive selection, use importance measures from the sub-model.
if (recursive) {
impvar <-
(1:length(imp.idx))[order(sub.rf$importance[,1], decreasing=TRUE)]
}
NULL
}
NULL
}
## cat("\n")
error.cv <- sapply(cv.pred, function(x) mean(x - trainy*log(x+(x==0))))
names(error.cv) <- names(cv.pred) <- n.var
list(n.var=n.var, error.cv=error.cv, predicted=cv.pred)
}
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