metaforest: Exploring Heterogeneity in Meta-Analysis using Random Forests
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 (Van Lissa, 2020). 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.
| Version: |
0.1.4 |
| Depends: |
R (≥ 3.5.0), ggplot2, metafor, ranger, data.table, methods |
| Imports: |
gtable, grid |
| Suggests: |
testthat, caret, knitr, rmarkdown, covr |
| Published: |
2024-01-26 |
| DOI: |
10.32614/CRAN.package.metaforest |
| Author: |
Caspar J. van Lissa |
| Maintainer: |
Caspar J. van Lissa <c.j.vanlissa at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Citation: |
metaforest citation info |
| Materials: |
README NEWS |
| In views: |
MetaAnalysis |
| CRAN checks: |
metaforest results |
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