var_split_factor

Split function to build the two daughter nodes from factor predictor

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

var_split_factor function

<dl><dt>X</dt>
<dd>Input data</dd>
<dt>Y</dt>
<dd>Outcome data</dd>
<dt>cause</dt>
<dd>(Only with competing events) Number indicates the event of interest.</dd>
<dt>nodesize</dt>
<dd>Minimal number of subjects required in both child nodes to split. Cannot be smaller than 1.</dd></dl>

Arguments

Split function to build the two daughter nodes from factor predictor — var_split_factor

<dl>

<dt>X</dt>
<dd>Input data</dd>


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


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


<dt>nodesize</dt>
<dd>Minimal number of subjects required in both child nodes to split. Cannot be smaller than 1.</dd>

</dl>

var_split_factor: Split function to build the two daughter nodes from factor predictor

Description

Usage

Arguments