subjectSplitter

An object created using createStudyPopulation().

population

A real number between 0 and 1 indicating the test set fraction of the data

test

A real number between 0 and 1 indicating the train set fraction of the data.
If not set train is equal to 1 - test

train

An integer &gt;= 1 specifying the number of folds used in cross validation

nfold

If set a fixed seed is used, otherwise a random split is performed

seed

Split data when patients are in the data multiple times such that the same patient is always either in the 
train set or the test set (the same patient cannot be in both the test and train set at different times)

A package for creating patient level prediction models. Given a
cohort of interest and an outcome of interest, the package can use data in the
OMOP Common Data Model to build a large set of features. These features can then
be assessed to fit a predictive model using a number of machine learning algorithms.
Several performance measures are implemented for model evaluation.

Jenna Reps

PatientLevelPrediction

Package for patient level prediction using data in the OMOP Common Data
Model

Martijn Schuemie

Marc Suchard

Patrick Ryan

Peter Rijnbeek

subjectSplitter function

Split data when patients are in the data multiple times such that the same patient is always either in the 
train set or the test set (the same patient cannot be in both the test and train set at different times) — subjectSplitter

Split data when patients are in the data multiple times such that the same patient is always either in the 
train set or the test set (the same patient cannot be in both the test and train set at different times)

subjectSplitter: Split data when patients are in the data multiple times such that the same patient is always either in the train set or the test set (the same patient cannot be in both the test and train set at different times)

Description

Usage

Arguments

Value

Details