impurity_split

internal

Based on random forest principle, 'DynForest' is able to include
multiple longitudinal predictors to provide individual predictions.
Longitudinal predictors are modeled through the random forest. The
methodology is fully described for a survival outcome in:
Devaux, Helmer, Genuer & Proust-Lima (2023)
<doi: 10.1177/09622802231206477>.

Anthony Devaux

DynForest

Random Forest with Multivariate Longitudinal Predictors

Robin Genuer

Cécile Proust-Lima

Louis Capitaine

impurity_split function

<dl><dt>Y</dt>
<dd>Outcome data</dd>
<dt>split</dt>
<dd>Vector containing the subjects groups</dd>
<dt>cause</dt>
<dd>(Only with competing events) Number indicates the event of interest.</dd></dl>

Arguments

Impurity Split — impurity_split

<dl>

<dt>Y</dt>
<dd>Outcome data</dd>


<dt>split</dt>
<dd>Vector containing the subjects groups</dd>


<dt>cause</dt>
<dd>(Only with competing events) Number indicates the event of interest.</dd>

</dl>

Impurity Split

impurity_split: Impurity Split

Description

Usage

Arguments