An R package for Bayesian meta-analysis that accounts for publication
bias or p-hacking.
publipha is an package for doing Bayesian meta-analysis that accounts for publication bias or p-hacking. Its main functions are:
psma does random effects meta-analysis under publication bias with a
one-sided p-value based selection probability. The model is
roughly the same as that of (Hedges,
1992)
phma does random effects meta-analysis under a certain model of
p-hacking with a one-sided p-value based propensity to p-hack.
This is based on the forthcoming paper of by Moss and De Bin
(2019).cma does classical random effects meta-analysis with the same priors
as psma and cma.Use the following command from inside R:
# install.packages("devtools")
# devtools::install_github("JonasMoss/publipha")
install.packages("publipha", repos = c('https://jonasmoss.r-universe.dev', 'https://cloud.r-project.org'))
Call the library function and use it like a barebones metafor::rma.
The alpha tells psma or phma where they should place the cutoffs
for significance.
library("publipha")
# Publication bias model
set.seed(313) # For reproducibility
model_psma = publipha::psma(yi = yi,
vi = vi,
alpha = c(0, 0.025, 0.05, 1),
data = metadat::dat.bangertdrowns2004)
# p-hacking model
set.seed(313)
model_phma = publipha::phma(yi = yi,
vi = vi,
alpha = c(0, 0.025, 0.05, 1),
data = metadat::dat.bangertdrowns2004)
# Classical model
set.seed(313)
model_cma = publipha::cma(yi = yi,
vi = vi,
alpha = c(0, 0.025, 0.05, 1),
data = metadat::dat.bangertdrowns2004)
You can calculate the posterior means of the meta-analytic mean with
extract_theta0:
extract_theta0(model_psma)
#> [1] 0.1277197
extract_theta0(model_cma)
#> [1] 0.2212093
If you wish to plot a histogram of the posterior distribution of tau,
the standard deviation of the effect size distribution, you can do it
like this:
extract_tau(model_psma, hist)
If you encounter a bug, have a feature request or need some help, open a Github issue. Create a pull requests to contribute.