createExistingSplitSettings

Create the settings for defining how the plpData are split into
test/validation/train sets using an existing split - good to use for
reproducing results from a different run

A user friendly way to create patient level prediction models using
the Observational Medical Outcomes Partnership Common Data Model. Given a cohort
of interest and an outcome of interest, the package can use data in the Common
Data Model to build a large set of features. These features can then be used to
fit a predictive model with a number of machine learning algorithms. This is
further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

Egill Fridgeirsson

PatientLevelPrediction

Develop Clinical Prediction Models Using the Common Data Model

Jenna Reps

Martijn Schuemie

Marc Suchard

Patrick Ryan

Peter Rijnbeek

Observational Health Data Science and Informatics 

createExistingSplitSettings function

<dl><dt>splitIds</dt>
<dd>(data.frame) A data frame with rowId and index columns of
type integer/numeric. Index is -1 for test set, positive integer for train
set folds</dd></dl>

Arguments

Create the settings for defining how the plpData are split into
test/validation/train sets using an existing split - good to use for
reproducing results from a different run — createExistingSplitSettings

<dl>

<dt>splitIds</dt>
<dd>(data.frame) A data frame with rowId and index columns of
type integer/numeric. Index is -1 for test set, positive integer for train
set folds</dd>

</dl>

Create the settings for defining how the plpData are split into
test/validation/train sets using an existing split - good to use for
reproducing results from a different run

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createExistingSplitSettings: Create the settings for defining how the plpData are split into test/validation/train sets using an existing split - good to use for reproducing results from a different run

Description

Usage

Value

Arguments

Examples