.partition

Partition the test set based on a trained tree

internal

Meta-CART integrates classification and regression trees (CART) into meta-analysis. Meta-CART is a flexible approach to identify interaction effects between moderators in meta-analysis. The method is described in Dusseldorp et al. (2014) <doi:10.1037/hea0000018> and Li et al. (2017) <doi:10.1111/bmsp.12088>.

Juan Claramunt Gonzalez

metacart

Meta-CART: A Flexible Approach to Identify Moderators in
Meta-Analysis

Xinru Li

Elise Dusseldorp

Kaihua Liu

Juan Claramunt

Jacqueline Meulman

.partition function

<dl><dt>x1</dt>
<dd>the tree component of the REmrt object</dd>
<dt>x2</dt>
<dd>the moderators in the test set</dd>
<dt>x3</dt>
<dd>indicates whether a moderator is numeric or not</dd>
<dt>x4</dt>
<dd>the index vector of the spliting moderators</dd>
<dt>x5</dt>
<dd>the list of split points</dd>
<dt>x6</dt>
<dd>the moderators in the training set</dd></dl>

Arguments

Partition the test set based on a trained tree — .partition

<dl>

<dt>x1</dt>
<dd>the tree component of the REmrt object</dd>


<dt>x2</dt>
<dd>the moderators in the test set</dd>


<dt>x3</dt>
<dd>indicates whether a moderator is numeric or not</dd>


<dt>x4</dt>
<dd>the index vector of the spliting moderators</dd>


<dt>x5</dt>
<dd>the list of split points</dd>


<dt>x6</dt>
<dd>the moderators in the training set</dd>

</dl>

.partition: Partition the test set based on a trained tree

Description

Usage

Arguments