Description Usage Arguments Examples
Bayesian meta-analysis assuming that the effect size d varies across studies with standard deviation τ (i.e., a random-effects 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 log-marginal likelihood: either by numerical
integration ( |
summarize |
how to estimate parameter summaries (mean, median, SD,
etc.): Either by numerical integration ( |
ci |
probability for the credibility/highest-density 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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