CRAN
metaforest 0.1.2
Exploring Heterogeneity in Meta-Analysis using Random Forests
Released May 31, 2018 by Caspar J. van Lissa
Dependencies
metafor 2.0-0 gtable 0.2.0 mmpf 0.0.4 ranger 0.10.0 ggplot2 2.2.1
Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates, even with as little as 20 studies (Van Lissa, in preparation). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.
Installation
Maven
This package can be included as a dependency from a Java or Scala project by including
the following your project's pom.xml file.
Read more
about embedding Renjin in JVM-based projects.
<dependencies>
<dependency>
<groupId>org.renjin.cran</groupId>
<artifactId>metaforest</artifactId>
<version>0.1.2-b1</version>
</dependency>
</dependencies>
<repositories>
<repository>
<id>bedatadriven</id>
<name>bedatadriven public repo</name>
<url>https://nexus.bedatadriven.com/content/groups/public/</url>
</repository>
</repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:metaforest')
Test Results
This package was last tested against Renjin 0.9.2644 on Jun 2, 2018.