Create train and test splits for cross-validation

Creates train and test splits for cross-validation by handling multiple data types and supporting k-fold, leave-one-out (LOO), and leave-percentage-out (LPO) methods. Handles missing values and maintains data structure across multiple datasets.

Usage

create_train_test_indices(
  data_list,
  cv_type = c("k-fold", "loo", "lpo"),
  k = 5,
  percentage = 20,
  number_folds = 10
)

Arguments

data_list

A list of datasets, one per likelihood. Each dataset can be a data.frame, SpatialPointsDataFrame, or metric_graph_data object

cv_type

Type of cross-validation: "k-fold", "loo", or "lpo". Default is "k-fold"

k

Number of folds for k-fold CV. Default is 5

percentage

Training data percentage for LPO CV (1-99). Default is 20

number_folds

Number of folds for LPO CV. Default is 10

Value

A list where each element contains:

train

Indices for training data mapped to original datasets

test

Indices for test data mapped to original datasets

Details

The function handles NA values by removing rows with any missing values before creating splits. For multiple datasets, indices are mapped back to their original positions in each dataset.