Create Cross-Validation Folds from a Hyperdesign Object

Creates cross-validation folds by splitting the data based on specified design variables. Each fold consists of a training set (analysis) and a test set (assessment).

Usage

# S3 method for class 'hyperdesign'
fold_over(
  x,
  ...,
  inclusion_condition = list(),
  exclusion_condition = list(),
  preserve_row_ids = FALSE
)

Arguments

x

A hyperdesign object

...

Unquoted names of variables to split on (e.g., condition, subject, run)

inclusion_condition

Optional list specifying values to include in assessment sets

exclusion_condition

Optional list specifying values to exclude from assessment sets

preserve_row_ids

Logical; if `TRUE`, carry original source row ids into fold `analysis` and `assessment` designs via a reserved `.orig_index` column. Matching `held_out$row_ids` metadata is also included.

Value

A foldlist object containing the cross-validation folds

Details

The function creates folds by splitting the data based on unique combinations of the specified variables. For each fold, one combination is held out as the assessment set, while the rest form the analysis set.

Important considerations: * If a splitting variable is confounded with blocks (e.g., each subject is in a separate block), the function will fail as there would be no training data available for that block. * Numeric variables (like run numbers) are handled by converting them to factors for splitting. * The function preserves the design structure within each fold.

Examples

d1 <- multidesign(matrix(rnorm(10*5), 10, 5),
                  data.frame(condition = rep(c("A","B"), 5), run = rep(1:5, 2)))
d2 <- multidesign(matrix(rnorm(10*5), 10, 5),
                  data.frame(condition = rep(c("A","B"), 5), run = rep(1:5, 2)))
hd <- hyperdesign(list(d1, d2))

# Leave-one-block-out folds
folds_block <- fold_over(hd)

# Fold by condition within blocks
folds_cond <- fold_over(hd, condition)