A collection of functions to build the training and validation sets.
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splitSample(object, percent.include, ...) splitStratify(object, percent.include, colBy = NULL, bin = rep(FALSE, length(colBy)), breaks = rep(list(NA), length(colBy)), ...) ## S4 method for signature 'ExprsArray' splitSample(object, percent.include, ...) ## S4 method for signature 'ExprsArray' splitStratify(object, percent.include, colBy = NULL, bin = rep(FALSE, length(colBy)), breaks = rep(list(NA), length(colBy)), ...)
Specifies the percent of the total number of subjects to include in the training set.
Specifies a vector of column names by which to stratify in
addition to class labels annotation. If
A logical vector indicating whether to bin the respective
A list. Each element of the list should correspond to a
splitSample builds a training and validation set by randomly sampling
the subjects found within the
ExprsArray object. Note that this method
is not truly random. Instead,
splitSample iterates through the random sampling
process until it settles on a solution such that both the training and validation set
contain at least one subject for each class label. If this method finds no solution
after 10 iterations, the function will post an error. Set
percent.include = 100
to skip random sampling and return a
NULL validation set. Additional arguments
replace = TRUE) passed along to
sample. This method works well
for all (i.e., binary and multi-class)
splitStratify builds a training and validation set through a stratified
random sampling process. This function utilizes the
strata function from the
sampling package as well as the
cut function from the base package. The latter
function provides a means by which to bin continuous data prior to stratified random
sampling. We refer the user to the parameter descriptions to learn the specifics of
how to apply binning, although the user might find it easier to instead bin
annotations beforehand. When applied to an
ExprsMulti object, this function
stratifies subjects across all classes found in that dataset.
Returns a list of two
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