HEMDAG (version 2.1.3)

do.unstratified.cv.data: Unstratified cross-validation

Description

This function splits a dataset in k-fold in an unstratified way (that is a fold may not have an equal amount of positive and negative examples). This function is used to perform k-fold cross-validation experiments in a hierarchical correction contest where splitting dataset in a stratified way is not needed.

Usage

do.unstratified.cv.data(S, kk = 5, seed = NULL)

Arguments

S

matrix of the flat scores. It must be a named matrix, where rows are example (e.g. genes) and columns are classes/terms (e.g. HPO terms)

kk

number of folds in which to split the dataset (def. k=5)

seed

seed for the random generator. If NULL (def.) no initialization is performed

Value

a list with \(k=kk\) components (folds). Each component of the list is a character vector contains the index of the examples, i.e. the index of the rows of the matrix S

Examples

# NOT RUN {
data(scores);
foldIndex <- do.unstratified.cv.data(S, kk=5, seed=23);
# }