stratified 10 fold cross validation

Question

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?

Answer 1

I found splitTools is pretty useful, hope the vignette https://cran.r-project.org/web/packages/splitTools/vignettes/splitTools.html can help anyone interested in this topic.

> y <- rep(c(letters[1:4]), each = 5)
> y
 [1] "a" "a" "a" "a" "a" "b" "b" "b" "b" "b" "c" "c" "c" "c" "c" "d" "d" "d" "d" "d"
> create_folds(y)
$Fold1
 [1]  1  2  3  5  6  7  8 10 12 13 14 15 17 18 19 20

$Fold2
 [1]  1  2  4  5  6  8  9 10 11 12 13 14 16 17 19 20

$Fold3
 [1]  2  3  4  5  6  7  9 10 11 12 13 15 16 17 18 20

$Fold4
 [1]  1  2  3  4  7  8  9 10 11 13 14 15 16 18 19 20

$Fold5
 [1]  1  3  4  5  6  7  8  9 11 12 14 15 16 17 18 19

> create_folds(y, m_rep = 3)
$Fold1.Rep1
 [1]  1  2  4  5  6  7  8 10 11 12 13 15 16 17 19 20

$Fold2.Rep1
 [1]  2  3  4  5  6  8  9 10 11 12 13 14 16 17 18 20

$Fold3.Rep1
 [1]  1  2  3  5  7  8  9 10 11 12 14 15 17 18 19 20

$Fold4.Rep1
 [1]  1  2  3  4  6  7  9 10 11 13 14 15 16 18 19 20

$Fold5.Rep1
 [1]  1  3  4  5  6  7  8  9 12 13 14 15 16 17 18 19

$Fold1.Rep2
 [1]  1  2  3  5  6  8  9 10 11 12 13 14 16 17 18 19

$Fold2.Rep2
 [1]  1  2  3  4  6  7  8 10 11 12 14 15 17 18 19 20

$Fold3.Rep2
 [1]  2  3  4  5  6  7  8  9 12 13 14 15 16 17 19 20

$Fold4.Rep2
 [1]  1  3  4  5  7  8  9 10 11 13 14 15 16 17 18 20

$Fold5.Rep2
 [1]  1  2  4  5  6  7  9 10 11 12 13 15 16 18 19 20

$Fold1.Rep3
 [1]  1  2  3  4  6  7  9 10 11 12 13 15 16 18 19 20

$Fold2.Rep3
 [1]  2  3  4  5  6  8  9 10 11 12 13 14 16 17 18 19

$Fold3.Rep3
 [1]  1  2  4  5  6  7  8  9 11 12 14 15 16 17 19 20

$Fold4.Rep3
 [1]  1  2  3  5  7  8  9 10 12 13 14 15 17 18 19 20

$Fold5.Rep3
 [1]  1  3  4  5  6  7  8 10 11 13 14 15 16 17 18 20

Answer 2

createFolds method of caret package performs a stratified partitioning. Here is a paragraph from the help page:

... The random sampling is done within the levels of y (=outcomes) when y is a factor in an attempt to balance the class distributions within the splits.

Here is the answer of your problem:

library(caret)
folds <- createFolds(factor(data$true.presence), k = 10, list = FALSE)

and the proportions:

> library(plyr)
> data$fold <- folds
> ddply(data, 'fold', summarise, prop=mean(true.presence))
     fold      prop
1       1 0.5000000
2       2 0.5050505
3       3 0.5000000
4       4 0.5000000
5       5 0.5000000
6       6 0.5049505
7       7 0.5000000
8       8 0.5049505
9       9 0.5000000
10     10 0.5050505

Answer 3

@joran is right (regarding his assumption (b)). dismo::kfold() is what you are looking for.

So using data from the initial question:

require(dismo)
folds <- kfold(data, k=10, by=data$true.presence)

gives a vector of length nrow(data) containing the fold association of each row of data. Hence, data[fold==1,] returns the 1st fold and data[fold!=1,] can be used for validation.

edit 6/2018: I strongly support using the caret package as recommended by @gkcn. It is better integrated in the tidyverse workflow and more actively developed. Go with that!