bootstrap_blocked_cross_validation | R Documentation |
Bootstrap Blocked Cross-Validation Specification
This function constructs a cross-validation specification using a predefined blocking variable.
This function constructs a cross-validation specification using a predefined blocking variable, dividing each block into a specified number of folds.
This function constructs a cross-validation specification that uses a user-supplied set of training and test indices.
bootstrap_blocked_cross_validation(block_var, nreps = 10, weights = NULL)
blocked_cross_validation(block_var)
sequential_blocked_cross_validation(block_var, nfolds = 2, nreps = 4)
custom_cross_validation(sample_set)
block_var |
An integer vector indicating the cross-validation blocks. Each block is indicated by a unique integer. |
nreps |
The number of repetitions for the cross-validation procedure. |
weights |
A numeric vector of the same length as 'block_var', representing the weights for each sample. Higher weights indicate that observations will be sampled more often. If not provided, all samples are treated as equally likely. |
nfolds |
The number of folds to divide each sequence of trials within a block. |
sample_set |
A list of training and test sample indices. Each element of the list must be a named list with two elements: "train" and "test". |
This function constructs a cross-validation specification using a predefined blocking variable and creates bootstrap resamples within the blocks.
The function first checks if the provided weights are non-negative and normalizes them to sum to 1. It then constructs a list containing the block variable, number of folds, block indices, number of repetitions, and weights. The output list is assigned the class '"bootstrap_blocked_cross_validation"', '"cross_validation"', and '"list"'.
The function constructs a list containing the block variable, number of folds, and block indices. The output list is assigned the class '"blocked_cross_validation"', '"cross_validation"', and '"list"'.
The function constructs a list containing the block variable, number of folds, number of repetitions, and block indices. The output list is assigned the class '"sequential_blocked_cross_validation"', '"cross_validation"', and '"list"'.
The custom_cross_validation class allows users to define their own cross-validation structure by providing a set of training and test indices. This can be useful in situations where the standard cross-validation methods (e.g., k-fold, leave-one-out) do not adequately represent the desired validation structure.
The function constructs a list containing the sample set and the number of folds, derived from the length of the sample set. The output list is assigned the class '"custom_cross_validation"', '"cross_validation"', and '"list"'.
A list containing the cross-validation specification, with class attributes "bootstrap_blocked_cross_validation", "cross_validation", and "list".
A list containing the cross-validation specification, with class attributes "blocked_cross_validation", "cross_validation", and "list".
A list containing the cross-validation specification, with class attributes "sequential_blocked_cross_validation", "cross_validation", and "list".
A list containing the custom cross-validation specification, with class attributes "custom_cross_validation", "cross_validation", and "list".
block_var <- rep(1:5, each=50)
weights <- runif(length(block_var))
weights[1] = 0
cval <- bootstrap_blocked_cross_validation(block_var, weights=weights)
X <- matrix(rnorm(length(block_var) * 10), length(block_var), 10)
y <- rep(letters[1:5], length.out=length(block_var))
sam <- crossval_samples(cval, as.data.frame(X), y)
block_var <- rep(1:5, each=50)
cval <- blocked_cross_validation(block_var)
X <- matrix(rnorm(length(block_var) * 10), length(block_var), 10)
y <- rep(letters[1:5], length.out=length(block_var))
sam <- crossval_samples(cval, as.data.frame(X), y)
block_var <- rep(1:5, each=50)
nfolds <- 2
nreps <- 4
cval <- sequential_blocked_cross_validation(block_var, nfolds, nreps)
X <- matrix(rnorm(length(block_var) * 10), length(block_var), 10)
y <- rep(letters[1:5], length.out=length(block_var))
sam <- crossval_samples(cval, as.data.frame(X), y)
sample_set <- list(
list(train = 1:80, test = 81:100),
list(train = 1:60, test = 61:100),
list(train = 1:40, test = 41:100)
)
cval <- custom_cross_validation(sample_set)
X <- matrix(rnorm(100 * 10), 100, 10)
y <- rep(letters[1:4], length.out=100)
sam <- crossval_samples(cval, as.data.frame(X), y)
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