personSplitter

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 into random subsets stratified by class

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.

personSplitter: Split data into random subsets stratified by class

Description

Usage

Arguments

Value

Details