I have made a start to create some training and test sets using 10 fold crossvalidation for an artificial dataset:

```
rows <- 1000

X1<- sort(runif(n = rows, min = -1, max =1))
occ.prob <- 1/(1+exp(-(0.0 + 3.0*X1)))
true.presence <- rbinom(n = rows, size = 1, prob = occ.prob)

# combine data as data frame and save
data <- data.frame(X1, true.presence)

id <- sample(1:10,nrow(data),replace=TRUE)
ListX <- split(data,id) 
fold1 <- data[id==1,] 
fold2 <- data[id==2,] 
fold3 <- data[id==3,] 
fold4 <- data[id==4,] 
fold5 <- data[id==5,] 
fold6 <- data[id==6,] 
fold7 <- data[id==7,] 
fold8 <- data[id==8,] 
fold9 <- data[id==9,] 
fold10 <- data[id==10,] 

trainingset <- subset(data, id %in% c(2,3,4,5,6,7,8,9,10))
testset <- subset(data, id %in% c(1))

```

I am just wondering whether there are easier ways to achieve this and how I could perform stratified crossvalidation which ensures that the class priors (true.presence) are roughly the same in all folds?