split_cv: Cross-Validation Split Generator

In mintyr: Streamlined Data Processing Tools for Genomic Selection

Cross-Validation Split Generator

Description

A robust cross-validation splitting utility for multiple datasets with advanced stratification and configuration options.

Usage

split_cv(
  split_dt,
  v = 10,
  repeats = 1,
  strata = NULL,
  breaks = 4,
  pool = 0.1,
  ...
)

Arguments

split_dt	list of input datasets Must contain data.frame or data.table elements Supports multiple dataset processing Cannot be empty
v	The number of partitions of the data set.
repeats	The number of times to repeat the V-fold partitioning.
strata	A variable in data (single character or name) used to conduct stratified sampling. When not NULL, each resample is created within the stratification variable. Numeric strata are binned into quartiles.
breaks	A single number giving the number of bins desired to stratify a numeric stratification variable.
pool	A proportion of data used to determine if a particular group is too small and should be pooled into another group. We do not recommend decreasing this argument below its default of 0.1 because of the dangers of stratifying groups that are too small.
...	These dots are for future extensions and must be empty.

Details

Advanced Cross-Validation Mechanism:

1. Input dataset validation

2. Stratified or unstratified sampling

3. Flexible fold generation

4. Train-validate set creation

Sampling Strategies:

• Supports multiple dataset processing

• Handles stratified and unstratified sampling

• Generates reproducible cross-validation splits

Value

list of data.table objects containing:

• splits: Cross-validation split objects

• train: Training dataset subsets

• validate: Validation dataset subsets

Note

Important Constraints:

• Requires non-empty input datasets

• All datasets must be data.frame or data.table

• Strata column must exist if specified

• Computational resources impact large dataset processing

See Also

• rsample::vfold_cv() Core cross-validation function

Examples

# Prepare example data: Convert first 3 columns of iris dataset to long format and split
dt_split <- w2l_split(data = iris, cols2l = 1:3)
# dt_split is now a list containing 3 data tables for Sepal.Length, Sepal.Width, and Petal.Length

# Example 1: Single cross-validation (no repeats)
split_cv(
  split_dt = dt_split,  # Input list of split data
  v = 3,                # Set 3-fold cross-validation
  repeats = 1           # Perform cross-validation once (no repeats)
)
# Returns a list where each element contains:
# - splits: rsample split objects
# - id: fold numbers (Fold1, Fold2, Fold3)
# - train: training set data
# - validate: validation set data

# Example 2: Repeated cross-validation
split_cv(
  split_dt = dt_split,  # Input list of split data
  v = 3,                # Set 3-fold cross-validation
  repeats = 2           # Perform cross-validation twice
)
# Returns a list where each element contains:
# - splits: rsample split objects
# - id: repeat numbers (Repeat1, Repeat2)
# - id2: fold numbers (Fold1, Fold2, Fold3)
# - train: training set data
# - validate: validation set data

mintyr documentation built on April 4, 2025, 2:56 a.m.