var_split_num

Split function to build the two daughter nodes from numeric predictors

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_num function

<dl><dt>X</dt>
<dd>Input data</dd>
<dt>Y</dt>
<dd>Outcome data</dd>
<dt>nsplit_option</dt>
<dd>A character indicates how the values are chosen to build the two groups for the splitting rule (only for continuous predictors). Values are chosen using deciles (<code>nsplit_option</code>="quantile") or randomly (<code>nsplit_option</code>="sample"). Default value is "quantile".</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 numeric predictors — var_split_num

<dl>

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


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


<dt>nsplit_option</dt>
<dd>A character indicates how the values are chosen to build the two groups for the splitting rule (only for continuous predictors). Values are chosen using deciles (<code>nsplit_option</code>="quantile") or randomly (<code>nsplit_option</code>="sample"). Default value is "quantile".</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_num: Split function to build the two daughter nodes from numeric predictors

Description

Usage

Arguments