balancedSplit: Split a dataset into training and testing sets, balancing a...

In CrossValidate: Classes and Methods for Cross Validation of "Class Prediction" Algorithms

Description

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.

Usage

balancedSplit(fac, size)

Arguments

fac	A factor that should be balanced between the two subsets.
size	A number between 0 and 1 indicating the fraction of the dataset to be used for training.

Details

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.

Value

Returns a logical vector with length equal to the length of fac. TRUE values designate samples selected for the training set.

Author(s)

Kevin R. Coombes <[e-mail]>

See Also

CrossValidate, CrossValidate-class.

Examples

nFeatures <- 40
nSamples <- 2*10
dataset <- matrix(rnorm(nSamples*nFeatures), ncol=nSamples)
groups <- factor(rep(c("A", "B"), each=10))
balancedSplit(dataset, groups)

Example output

Loading required package: Modeler
Loading required package: ClassDiscovery
Loading required package: cluster
Loading required package: oompaBase
Loading required package: ClassComparison
  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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CrossValidate documentation built on May 7, 2019, 3 a.m.