dat.balanced

Data simulated from a true model with pure interactions between two
moderators: x1, x2. If x1 = 0 and x2 = 1 or x1 = 1 and x2 = 0, 
the true effect size is 0.50. Otherwise, the true effect size is 0.

datasets

simulated

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

dat.balanced function

A data frame of 60 studies with 4 moderators<ul>
<li>efk: The effect size of each study expressed as Hedges' g</li>
<li>vark: The sampling variance of the effect size</li>
<li>x1 to x4: Four randomly generated moderators. x1, x2, and x4 are dichotomous variables,
 x3 is a continuous variable generated from uniform distribution.</li>
</ul>

dat.balanced: A simulated meta-analytic data set with balanced pure interaction effects

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