library(mlbench)
library(randomForest)
data(BostonHousing2)
dane <- BostonHousing2
dane <- unique(dane)
rows <- nrow(dane)
dane <- dane[sample(rows),]
dane <- na.omit(dane)
n <- 10
div <- rows %/% n
mod <- rows %% n
sampleSizes <- vector(mode = "integer", length = n)
for (i in 1:n){
  if(i<=mod)
    sampleSizes[i] <- div+1
  else
    sampleSizes[i] <- div
}
pred <- data.frame()
test <- data.frame()
for (i in 1:n){
  begin <- sum(sampleSizes[0:(i-1)])
  end <- sum(sampleSizes[1:i])
  trainingset <- dane[c(0:(begin), (end+1):rows),]
  testset <- dane[(begin+1):end,]
  interesujace <- c(2:4, 7:19)
  trainingset <- na.omit(trainingset)
  model <- randomForest(x=trainingset[,interesujace], y=trainingset[,6], ntree=10)
  temp <- as.data.frame(predict(model, testset[,interesujace]))
  pred <- rbind(pred, temp)
  test <- rbind(test, as.data.frame(testset[,6]))
}

err <- sqrt(sum((test-pred)^2)/rows)