Split Sample Indexes into Training and Test Partitions for Cross-validation Taking Into Account Classes.

samplesSplits Creates two lists of lists. First has training samples, second has test samples for a range of different cross-validation schemes.

splitsTestInfo creates a table for tracking the permutation, fold number, or subset of each set of test samples. Useful for column-binding to the predictions, once they are unlisted into a vector.

Usage

samplesSplits(
  samplesSplits = c("k-Fold", "Permute k-Fold", "Permute Percentage Split",
    "Leave-k-Out"),
  permutations = 100,
  folds = 5,
  percentTest = 25,
  leave = 2,
  outcome
)

splitsTestInfo(
  samplesSplits = c("k-Fold", "Permute k-Fold", "Permute Percentage Split",
    "Leave-k-Out"),
  permutations = 100,
  folds = 5,
  percentTest = 25,
  leave = 2,
  splitsList
)

Arguments

samplesSplits: Default: "k-Fold". One of "k-Fold", "Permute k-Fold", "Permute Percentage Split", "Leave-k-Out".
permutations: Default: 100. An integer. The number of times the samples are permuted before splitting (repetitions).
folds: Default: 5. An integer. The number of folds to which the samples are partitioned to. Only relevant if samplesSplits is "k-Fold" or "Permute k-Fold".
percentTest: Default: 25. A positive number between 0 and 100. The percentage of samples to keep for the test partition. Only relevant if samplesSplits is "Permute Percentage Split".
leave: Default: 2. An integer. The number of samples to keep for the test set in leave-k-out cross-validation. Only relevant if samplesSplits is "Leave-k-Out".
outcome: A factor vector or Surv object containing the samples to be partitioned.
splitsList: The return value of the function samplesSplits.

Value

For samplesSplits, two lists of the same length. First is training partitions. Second is test partitions.

For splitsTestInfoTable, a table with a subset of columns "permutation", "fold" and "subset", depending on the cross-validation scheme specified.

Examples


classes <- factor(rep(c('A', 'B'), c(15, 5)))
splitsList <-samplesSplits(permutations = 1, outcome = classes)
splitsList
#> $train
#> $train[[1]]
#>  [1]  5  6  3 19  2 12 13 20  1 15  8 16  4  9 11 17
#> 
#> $train[[2]]
#>  [1]  7 14 10 18  2 12 13 20  1 15  8 16  4  9 11 17
#> 
#> $train[[3]]
#>  [1]  7 14 10 18  5  6  3 19  1 15  8 16  4  9 11 17
#> 
#> $train[[4]]
#>  [1]  7 14 10 18  5  6  3 19  2 12 13 20  4  9 11 17
#> 
#> $train[[5]]
#>  [1]  7 14 10 18  5  6  3 19  2 12 13 20  1 15  8 16
#> 
#> 
#> $test
#> $test[[1]]
#> [1]  7 14 10 18
#> 
#> $test[[2]]
#> [1]  5  6  3 19
#> 
#> $test[[3]]
#> [1]  2 12 13 20
#> 
#> $test[[4]]
#> [1]  1 15  8 16
#> 
#> $test[[5]]
#> [1]  4  9 11 17
#> 
#> 
splitsTestInfo(permutations = 1, splitsList = splitsList)
#>       fold
#>  [1,]    1
#>  [2,]    1
#>  [3,]    1
#>  [4,]    1
#>  [5,]    2
#>  [6,]    2
#>  [7,]    2
#>  [8,]    2
#>  [9,]    3
#> [10,]    3
#> [11,]    3
#> [12,]    3
#> [13,]    4
#> [14,]    4
#> [15,]    4
#> [16,]    4
#> [17,]    5
#> [18,]    5
#> [19,]    5
#> [20,]    5