createTuningMetric

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 

createTuningMetric function

<dl><dt>fun</dt>
<dd>Function (or function name) that returns a single numeric score
when given a prediction data frame.</dd>
<dt>maximize</dt>
<dd>Logical; TRUE if larger is better.</dd>
<dt>name</dt>
<dd>Friendly name for logs and summaries.</dd>
<dt>funArgs</dt>
<dd>Optional named list of extra arguments passed to <code>fun</code>.</dd></dl>

Arguments

Create a tuning metric descriptor — createTuningMetric

<dl>

<dt>fun</dt>
<dd>Function (or function name) that returns a single numeric score
when given a prediction data frame.</dd>


<dt>maximize</dt>
<dd>Logical; TRUE if larger is better.</dd>


<dt>name</dt>
<dd>Friendly name for logs and summaries.</dd>


<dt>funArgs</dt>
<dd>Optional named list of extra arguments passed to <code>fun</code>.</dd>

</dl>

Create a tuning metric descriptor

createTuningMetric: Create a tuning metric descriptor

Description

Usage

Arguments