library(randomForest)
library(foreach)
library(cvTools)
set.seed(1234)
K =10
R = 3
cv <- cvFolds(NROW(iris),K=K,R=R)
grid <- expand.grid(ntree=c(10,100,200),mtry=c(3,4))
result <- foreach(g=1:NROW(grid),.combine = rbind) %do% {
  foreach(r=1:R,.combine = rbind) %do% {
    foreach(k=1:K,.combine = rbind) %do% {
      validation_idx <- cv$subsets[which(cv$which ==k ),r]
      train  <- iris[-validation_idx,]
      validation <- iris[validation_idx,]
      m <- randomForest(Species~.,
                        data=train,
                        ntree=grid[g,"ntree"],
                         mtry=grid[g,"mtry"])
      predicted <- predict(m,newdata=validation)
      precision <- sum(prediced == validation$Species) / NROW(predicted)
      return(data.frame(g=g, precision=precision))
    }
  }
}