stratifySample: Performs stratified cross validation sampling of a data...

In JL-Woodcock/stRatify: Simple Stratifiee Cross Validation for Binary Targets

Description

Generates cross validation folds in a stratified way.

Usage

stratifySample(data, targetVariable, nfolds = 5, uniqueID = NULL,
  keepData = FALSE, positiveResponse = 1, seed = NULL)

Arguments

data	A dataframe which contains the base data to be split.
targetVariable	A string giving the name of the binary variable to stratify by.
nfolds	An integer giving the number of cross validation folds.
uniqueID	A string giving the name of a unique row identifier. Note, if there is no identifier, the function will filter the dataset by index.
keepData	A Boolean indicating whether or not to keep the entire dataset along with extra columns indicating the folds.
positiveResponse	The value of the target variable which indicates a positive response. Must be 1 or 0.
seed	The seed value to use to reproduce results.

Value

An object of the class StratifiedXValS3.

Examples

stratifySample()

JL-Woodcock/stRatify documentation built on Nov. 5, 2019, 2:17 p.m.