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.
create_train_test_indices(
data_list,
cv_type = c("k-fold", "loo", "lpo"),
k = 5,
percentage = 20,
number_folds = 10
)A list where each element contains:
Indices for training data mapped to original datasets
Indices for test data mapped to original datasets
A list of datasets, one per likelihood. Each dataset can be a data.frame, SpatialPointsDataFrame, or metric_graph_data object
Type of cross-validation: "k-fold", "loo", or "lpo". Default is "k-fold"
Number of folds for k-fold CV. Default is 5
Training data percentage for LPO CV (1-99). Default is 20
Number of folds for LPO CV. Default is 10
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.