Description Usage Arguments Examples
Bayesian metaanalysis assuming that the effect size d varies across studies with standard deviation τ (i.e., a randomeffects model).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  meta_random(
y,
SE,
labels,
data,
d = prior("cauchy", c(location = 0, scale = 0.707)),
tau = prior("invgamma", c(shape = 1, scale = 0.15)),
rscale_contin = 0.5,
rscale_discrete = 0.707,
centering = TRUE,
logml = "integrate",
summarize = "stan",
ci = 0.95,
rel.tol = .Machine$double.eps^0.3,
logml_iter = 5000,
silent_stan = TRUE,
...
)

y 
effect size per study. Can be provided as (1) a numeric vector, (2)
the quoted or unquoted name of the variable in 
SE 
standard error of effect size for each study. Can be a numeric
vector or the quoted or unquoted name of the variable in 
labels 
optional: character values with study labels. Can be a
character vector or the quoted or unquoted name of the variable in

data 
data frame containing the variables for effect size 
d 

tau 

rscale_contin 
scale parameter of the JZS prior for the continuous covariates. 
rscale_discrete 
scale parameter of the JZS prior for discrete moderators. 
centering 
whether continuous moderators are centered. 
logml 
how to estimate the logmarginal likelihood: either by numerical
integration ( 
summarize 
how to estimate parameter summaries (mean, median, SD,
etc.): Either by numerical integration ( 
ci 
probability for the credibility/highestdensity intervals. 
rel.tol 
relative tolerance used for numerical integration using

logml_iter 
number of iterations (per chain) from the posterior
distribution of 
silent_stan 
whether to suppress the Stan progress bar. 
... 
further arguments passed to 
1 2 3 4 5 6 7 8 9 10 
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