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

In CrossVal: Classes and methods for cross validation of "class prediction" with microarrays or proteomics

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 certain binary outcome. This function implements such a balanced split.

Usage

balancedSplit(fac, size)

Arguments

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

Details

Stuff should go here

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

CrossVal, CrossVal-class, CrossVal-package.

Examples

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

CrossVal documentation built on May 2, 2019, 5:47 p.m.