library(plyr)
CVgroup <- function(k,datasize,seed){
  cvlist <- list()
  set.seed(seed)
  n <- rep(1:k,ceiling(datasize/k))[1:datasize]  
  temp <- sample(n,datasize)  
  x <- 1:k
  dataseq <- 1:datasize
  cvlist <- lapply(x,function(x) dataseq[temp==x]) 
  return(cvlist)
}
k <- 10
datasize <- nrow(shuju_xiugai)
cvlist <- CVgroup(k = k,datasize = datasize,seed = 11)
#cvlist

pred <- data.frame()   
#cvlist <- list()
progress.bar <- create_progress_bar("text")  
progress.bar$init(k)   
for (i in 1:k){
  train <- shuju_xiugai[-cvlist[[i]],] 
  test <- shuju_xiugai[cvlist[[i]],]
  model<- randomForest(data = train,PM2.5~.,method="REML")   
  prediction <- predict(model,subset(test,select = -PM2.5))   
  kcross <- rep(i,length(prediction))  
  temp <- data.frame(cbind(subset(test,select = PM2.5),prediction,kcross))
  pred <- rbind(pred,temp)  
  print(paste("randomForest"))  
  progress.bar$step() 
}