Description Usage Arguments Details Value Author(s) See Also Examples

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.

1 | ```
balancedSplit(fac, size)
``` |

`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. |

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
set.

Kevin R. Coombes <krc@silicovore.com>

`CrossValidate`

, `CrossValidate-class`

,
`CrossValidate-package`

.

1 2 3 4 5 |

CrossValidate documentation built on Aug. 4, 2017, 3:01 p.m.

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