Skip to content

Latest commit

 

History

History
executable file
·
137 lines (106 loc) · 5.43 KB

README.md

File metadata and controls

executable file
·
137 lines (106 loc) · 5.43 KB
Raw

metahelpr

The goal of metahelpr is to offer tutorials and helper functions for conducting meta-analysis using the metafor package (Viechtbauer 2010). The package contains some helper functions that I often use when I conduct meta-analysis, but I offer no guarantees that they’ll actually work for you because I’m not a particularly experienced programmer. Feel free to contribute and make them better and more robust.

Installation

You can install the development version of metahelpr from GitHub with:

# install.packages("remotes")
remotes::install_github("profandyfield/metahelpr")

Interactive tutorials

I recommend working through this playlist of tutorials on how to install, set up and work within and before starting the interactive tutorials.

  • meta_d: A tutorial on using the metafor package to conduct a meta-analysis using Cohen’s $\hat{d}$ as the effect size measure.
  • meta_r: A tutorial on using the metafor package to conduct a meta-analysis using Pearson’s $r$ as the effect size measure.

Data files

  • brewin_2024: Raw data from the meta-analysis by Brewin and Field (2024).
  • brewin_es: Reduced dataset from the meta-analysis by Brewin and Field (2024) that includes effect sizes.
  • pearce_2016: A selection of variables from the dataset from the meta-analysis by Pearce and Field (2016)

Helper functions

The package contains some (probably badly written and easily breakable) helper functions that I often use when I conduct meta-analysis.

  • d_from_r: estimate Cohen’s d based on a biserial correlation coefficient, or when a biserial correlation isn’t available the conversion uses the method described by Mathur and VanderWeele (2020).
  • d_to_g: converts Cohen’s d to the unbiased Hedges’ g.
  • forest_add_het_stats: Add heterogeneity statistics to a forest plot
  • forest_subgroups: plots a forest plot where effect sizes are split by subcategories of a factor.
  • get_mas: Fit individual meta-analyses for each level of a categorical predictor and (optionally) collate the results into a tabulated form for printing in a quarto document.
  • get_mod_mas: does the same thing as get_mas() but allows the user to specify a moderator/predictor within the individual meta-analyses. So, it fits individual meta-analyses models with a moderator specified using the rma.mv() function from the metafor package within each level of a categorical variable. Optionally, the results can be collated into a tabulated form for printing in a quarto document.
  • get_pbm: fits and collates publication bias models (optionally across categories of a predictor variable). It is assumed that you will supply two vectors of values one representing moderate publication bias and the other representing severe.
  • plot_bubble: create a bubble plot (using ggplot2) based on a regtest() object.
  • pooled_var: computes a full sample variance estimate based on means and variances from two subgroups.
  • regtest_tbl: puts the results of metafor::regtest() into a tibble for reporting.
  • report_het: collates information from heterogeneity tests and outputs text that summarizes the results in a format that will render nicely in quarto.
  • report_mod: outputs text that reports (and renders nicely in quarto/Rmarkdown) the omnibus statistical tests from a moderation model.
  • report_par_tbl: outputs a tibble of the table of coefficients of a meta-analyses object (rma or rma.mv) created using the metafor package. This function will mostly be useful for models containing predictors of effect sizes (so-called meta-regression).
  • report_pars: outputs text that reports (and renders nicely in quarto/Rmarkdown) the individual effects from a meta-analysis model.
  • var_d_from_r: Estimate the sampling variance of Cohen’s d based on a correlation coefficient

References

Brewin, C. R., and Andy P. Field. 2024. “Meta-Analysis Shows Trauma Memories in PTSD Lack Coherence: A Response to Taylor Et Al. (2022).” Clinical Psychological Science.

Mathur, Maya B., and Tyler J. VanderWeele. 2020. “A Simple, Interpretable Conversion from Pearson’s Correlation to Cohen’s <Em>d</Em> for Continuous Exposures.” Epidemiology 31 (2): e16–17. https://doi.org/10.1097/EDE.0000000000001111.

Pearce, Laura J., and Andy P. Field. 2016. “The Impact of ‘Scary’ TV and Film on Children’s Internalizing Emotions: A Meta-Analysis.” Human Communication Research 42 (January): 98–121. https://doi.org/doi:10.1111/hcre.12069.

Viechtbauer, Wolfgang. 2010. “Conducting Meta-Analyses in {R} with the {Metafor} Package.” https://doi.org/10.18637/jss.v036.i03.