Nothing

```
#-------Helper methods for creating training samples------------------------------------------------------
#'Make multiple samples of data
#'@name multisample
NULL
#'
#'\code{cross_fold}: Make 'folds' samples of the data, so \code{all(rbind(folds)==row.names(data))=TRUE}
#'@param data Data to sample
#'@param folds Number of folds to create
#'@param dependent The dependent variable in the data. Used only if \code{preserve_distribution=TRUE}
#'@param preserve_distribution Logical, only applicable if the dependent variable is a factor
#'@return A list of numeric vectors of length 'folds'
#'@rdname multisample
multisample.cross_fold <- function(data, folds=10, dependent, preserve_distribution=FALSE){
data_folds <- list()
# Folds should just be random
if(!preserve_distribution){
# Make a shuffled factor with values 1:folds of length (nrow(data))
split_unshuffled <- rep(1:folds, ceiling(nrow(data)/folds))
split_unshuffled <- split_unshuffled[1:nrow(data)]
# Shuffle the unshuffled vector to make a factor for splitting the data
split_factor <- sample(split_unshuffled)
# Actually split the data using the factor. Every fold is a character, so
# it needs to be converted to numeric
data_folds <- split(row.names(data), split_factor)
data_folds <- lapply(data_folds, as.numeric)
} else {
# The distribution of each fold should be as similar as possible
data_folds <- caret::createFolds(data[[dependent]], folds)
}
# For each fold, make a training set of the other folds
lapply(1:length(data_folds),
function(fold){
unlist(data_folds[-fold], use.names=FALSE)
})
}
#' Make random samples of the data
#'
#'\code{random}: Makes \code{iterations} random samples of size \code{holdout * nrow(data)}
# Arguments:
#'@param iterations Number of iterations to make
#'@param holdout The fraction of data to be used as holdout set
#'@rdname multisample
multisample.random <- function(data, holdout=0.2, iterations=10, dependent, preserve_distribution=FALSE){
lapply(1:iterations, function(x,
holdout,
dependent,
preserve_distribution
) {
train_index <- c()
if (preserve_distribution){
#The class distribution in train and test should be as similar as possible. Only makes sense for classification problems: regression has no classes.
train_index <- caret::createDataPartition(data[[dependent]], p=1-holdout, list=FALSE, times=1)
} else {
#The train and test sets should be constructed randomly by taking a sample. The size of the sample is rounded down, as the sample size should be an integer.
train_index <- sample(1:nrow(data), round((1-holdout) * nrow(data)))
}
train_index
},
# Set arguments to anonymous inner function
holdout=holdout,
dependent=dependent,
preserve_distribution=preserve_distribution
)
}
```

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