Implementation of K-fold cross validation

kfold.R

require(plyr)

kfold <- function(df, k, train.f, predict.f) {
  progress.bar <- create_progress_bar("text")
  progress.bar$init(k)
  
  N <- nrow(df)
  subsample.id <- sample(1:k, N, replace = TRUE)
  r <- vector(length = N)
  
  
  for (i in 1:k) {
    m <- train.f(df[subsample.id != i,])
    r[subsample.id == i] <- predict.f(m, df[subsample.id == i,])
    progress.bar$step()
  }
  return(r)
}


resp <- kfold(df.train, 5,
              function(d) {
                cforest(fml, data = d, controls=cforest_control(ntree=1000, mtry=5, mincriterion = qnorm(0.95)))
              },
              function(m, d) {
                predict(m, d, OOB=TRUE, type = "response")
              })