Helper functions for K-fold cross-validation
These functions can be used to generate indexes for use with K-fold cross-validation. See the Details section for explanations.
kfold_split_random(K = 10, N = NULL) kfold_split_stratified(K = 10, x = NULL) kfold_split_grouped(K = 10, x = NULL)
K |
The number of folds to use. |
N |
The number of observations in the data. |
x |
A discrete variable of length |
kfold_split_random() splits the data into K groups
of equal size (or roughly equal size).
For a categorical variable x kfold_split_stratified()
splits the observations into K groups ensuring that relative
category frequencies are approximately preserved.
For a grouping variable x, kfold_split_grouped() places
all observations in x from the same group/level together in
the same fold. The selection of which groups/levels go into which
fold (relevant when when there are more groups than folds) is
randomized.
An integer vector of length N where each element is an index in 1:K.
ids <- kfold_split_random(K = 5, N = 20) print(ids) table(ids) x <- sample(c(0, 1), size = 200, replace = TRUE, prob = c(0.05, 0.95)) table(x) ids <- kfold_split_stratified(K = 5, x = x) print(ids) table(ids, x) grp <- gl(n = 50, k = 15, labels = state.name) length(grp) head(table(grp)) ids_10 <- kfold_split_grouped(K = 10, x = grp) (tab_10 <- table(grp, ids_10)) colSums(tab_10) ids_9 <- kfold_split_grouped(K = 9, x = grp) (tab_9 <- table(grp, ids_9)) colSums(tab_9)
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