createDefaultSplitSetting: Create the settings for defining how the plpData are split...

In OHDSI/PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

Create the settings for defining how the plpData are split into test/validation/train sets using default splitting functions (either random stratified by outcome, time or subject splitting)

Description

Create the settings for defining how the plpData are split into test/validation/train sets using default splitting functions (either random stratified by outcome, time or subject splitting)

Usage

createDefaultSplitSetting(
  testFraction = 0.25,
  trainFraction = 0.75,
  splitSeed = sample(1e+05, 1),
  nfold = 3,
  type = "stratified"
)

Arguments

testFraction	(numeric) A real number between 0 and 1 indicating the test set fraction of the data
trainFraction	(numeric) A real number between 0 and 1 indicating the train set fraction of the data. If not set train is equal to 1 - test
splitSeed	(numeric) A seed to use when splitting the data for reproducibility (if not set a random number will be generated)
nfold	(numeric) An integer > 1 specifying the number of folds used in cross validation
type	(character) Choice of: 'stratified' Each data point is randomly assigned into the test or a train fold set but this is done stratified such that the outcome rate is consistent in each partition 'time' Older data are assigned into the training set and newer data are assigned into the test set 'subject' Data are partitioned by subject, if a subject is in the data more than once, all the data points for the subject are assigned either into the test data or into the train data (not both).

Details

Returns an object of class splitSettings that specifies the splitting function that will be called and the settings

Value

An object of class splitSettings

Examples

createDefaultSplitSetting(testFraction=0.25, trainFraction=0.75, nfold=3,
                          splitSeed=42)

OHDSI/PatientLevelPrediction documentation built on Feb. 14, 2025, 9:44 a.m.