crossv_bootstrap_block: Block Bootstrap Cross-Validation Data Preparation

In bbuchsbaum/rMVPA: Multivoxel Pattern Analysis in R

Block Bootstrap Cross-Validation Data Preparation

Description

This function prepares the data for block bootstrap cross-validation by dividing the dataset based on the provided block variable. It creates subsets of training and testing data for each block using bootstrap sampling within the training blocks, without performing any analysis or fitting models.

Usage

crossv_bootstrap_block(
  data,
  y,
  block_var,
  nreps = 5,
  id = ".id",
  weights = NULL
)

Arguments

data	A data frame containing the training data.
y	A response vector.
block_var	An integer vector defining the cross-validation blocks.
nreps	An integer specifying the number of bootstrap repetitions.
id	A character string specifying the identifier for the output data frame.
weights	An optional numeric vector of weights to be used for bootstrap sampling.

Details

The function first checks if the length of the 'block_var' vector matches the length of the response vector 'y'. It then creates a list of block indices and ensures there is more than one block to bootstrap. If weights are provided, the function splits the weights according to the block variable.

The function performs bootstrap sampling within the training blocks but keeps the test set fixed. For each block, it generates a list of training indices using bootstrap sampling and creates the corresponding training and testing data sets.

Value

A tibble containing the training and testing data, response vectors, and block IDs for each fold.

bbuchsbaum/rMVPA documentation built on April 23, 2024, 7:35 a.m.