When performing cross-validation on a dataset, it often becomes necessary to split the data into training and test sets that are balanced for a factor. This function implements such a balanced split.
A factor that should be balanced between the two subsets.
A number between 0 and 1 indicating the fraction of the dataset to be used for training.
This function randomly samples the same fraction of items from each level of a factor to include in a training set. In most cases, this will be a binary factor (and might even be the outcome that one wants to predict). However, the implementation works for factors with an arbitrary number of levels.
Returns a logical vector with length equal to the length of
fac. TRUE values designate samples selected for the training
Kevin R. Coombes <[email protected]>
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