[R-meta] Simple Bayesian meta-analysis in R
g@@p@r@|uk@c@ @end|ng |rom un|v|e@@c@@t
Fri Sep 27 17:44:49 CEST 2019
I'd like to conduct a Bayesian meta-analysis in R in order to support
equivalence between the effects of two design types (within each study).
E.g., Bayes factors would be perfect.
The metaBMA package seems to imply that I can get simple Bayes factors for
a moderator in a meta-analysis. (Most other Bayesian packages don't seem to
allow moderators.) But I don't find any info about how exactly to use it
and/or how to interpret the output.
Here is a simple example:
bayes_model = metaBMA::meta_random(
y = cohens_d~version,
SE = sed,
labels = study,
data = met_bf
"cohens_d" is the effect in each study, separately for each of the two
levels of "version", and I would simply want to know whether there is
substantial support for the equivalence between the "version" factors. Is
this model correct? And in any case, how can I get Bayes factors out of it
(or any direct proof of equivalence)?
A more detailed description of the question is here (where I was suggested
this mailing list):
I'd much appreciate any help.
[[alternative HTML version deleted]]
More information about the R-sig-meta-analysis