cross_validation: cross_validation

In DCAN-Labs/RFRF: Functional Random Forest in R

cross_validation

Description

this function is used to create folds of the training data for training the model

Usage

cross_validation(
  training_dataset = training_dataset,
  seed = seed,
  groups = groups,
  nfolds = nfolds
)

Arguments

training_dataset	the training data
seed	initialize a random number generator with a specific seed value
groups	information on how the stratified data will be grouped (name of column, index of column, vector)
nfolds	number of folds in the data if using split_data for cross validation

Examples

number_of_participants <- 10
covariance_matrix <- diag(2)
outcome_column <- 1
means <- c(0,0)
seed <- set.seed(7)
groups <- rep(1:2, each = 5) #information on how the stratified data will be grouped
nfolds <- 5
data <- simulate_data(number_of_participants,covariance_matrix,outcome_column,means)
randomized_data <- randomize(training_dataset=data,seed=seed)
stratified_data <- stratify_data(randomized_data=randomized_data, groups=groups)
folded_data <- split_data(stratified_data=stratified_data, nfolds=nfolds)

DCAN-Labs/RFRF documentation built on March 15, 2024, 2:33 p.m.