treepruner

Prune an initial rpart tree by "c-standard-error" rule.

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

treepruner function

<dl><dt>tree</dt>
<dd>A initial tree fitted by rpart, needs to an rpart object.</dd>
<dt>c</dt>
<dd>A scalar to prune the tree by selecting the tree with minum cross-validation error plus the standard error multiplied by c.</dd>
<dt>...</dt>
<dd>Additional arguments passed to prune.rpart().</dd></dl>

Arguments

Prune a tree — treepruner

<dl>

<dt>tree</dt>
<dd>A initial tree fitted by rpart, needs to an rpart object.</dd>


<dt>c</dt>
<dd>A scalar to prune the tree by selecting the tree with minum cross-validation error plus the standard error multiplied by c.</dd>


<dt>...</dt>
<dd>Additional arguments passed to prune.rpart().</dd>

</dl>

treepruner: Prune a tree

Description

Usage

Value

Arguments