personSplitter: Split data into random subsets stratified by class

In hxia/plp-git-demo: Package for patient level prediction using data in the OMOP Common Data Model

Description

Split data into random subsets stratified by class

Usage

personSplitter(population, test = 0.3, train = NULL, nfold = 3, seed = NULL)

Arguments

population	An object created using createStudyPopulation().
test	A real number between 0 and 1 indicating the test set fraction of the data
train	A real number between 0 and 1 indicating the train set fraction of the data. If not set train is equal to 1 - test
nfold	An integer >= 1 specifying the number of folds used in cross validation
seed	If set a fixed seed is used, otherwise a random split is performed

Details

Returns a dataframe of rowIds and indexes with a -1 index indicating the rowId belongs to the test set and a positive integer index value indicating the rowId's cross valiation fold within the train set.

Value

A dataframe containing the columns: rowId and index

hxia/plp-git-demo documentation built on March 19, 2021, 1:54 a.m.