pred.MMT

Predict the leaf by dropping down the subject in the tree

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

pred.MMT function

<dl><dt>tree</dt>
<dd>Tree object resulting from <code>Rtmax_surv</code> function</dd>
<dt>Longitudinal</dt>
<dd>A list of longitudinal predictors which should contain: <code>X</code> a dataframe with one row for repeated measurement and as many columns as markers; <code>id</code> is the vector of the identifiers for the repeated measurements contained in <code>X</code>; <code>time</code> is the vector of the measurement times contained in <code>X</code>.</dd>
<dt>Numeric</dt>
<dd>A list of numeric predictors which should contain: <code>X</code> a dataframe with as many columns as numeric predictors; <code>id</code> is the vector of the identifiers for each individual.</dd>
<dt>Factor</dt>
<dd>A list of factor predictors which should contain: <code>X</code> a dataframe with as many columns as factor predictors; <code>id</code> is the vector of the identifiers for each individual.</dd>
<dt>timeVar</dt>
<dd>A character indicating the name of time variable</dd></dl>

Arguments

Predict the leaf by dropping down the subject in the tree — pred.MMT

<dl>

<dt>tree</dt>
<dd>Tree object resulting from <code>Rtmax_surv</code> function</dd>


<dt>Longitudinal</dt>
<dd>A list of longitudinal predictors which should contain: <code>X</code> a dataframe with one row for repeated measurement and as many columns as markers; <code>id</code> is the vector of the identifiers for the repeated measurements contained in <code>X</code>; <code>time</code> is the vector of the measurement times contained in <code>X</code>.</dd>


<dt>Numeric</dt>
<dd>A list of numeric predictors which should contain: <code>X</code> a dataframe with as many columns as numeric predictors; <code>id</code> is the vector of the identifiers for each individual.</dd>


<dt>Factor</dt>
<dd>A list of factor predictors which should contain: <code>X</code> a dataframe with as many columns as factor predictors; <code>id</code> is the vector of the identifiers for each individual.</dd>


<dt>timeVar</dt>
<dd>A character indicating the name of time variable</dd>

</dl>

pred.MMT: Predict the leaf by dropping down the subject in the tree

Description

Usage

Arguments