"BiBMA.reg" ensemble implied by the specified priors
and formulacheck_setup prints summary of "RoBMA.reg" ensemble
implied by the specified prior distributions. It is useful for checking
the ensemble configuration prior to fitting all of the models.
check_setup.BiBMA(
priors_effect = prior(distribution = "student", parameters = list(location = 0, scale =
0.58, df = 4)),
priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1.77,
scale = 0.55)),
priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
0)),
priors_baseline = NULL,
priors_baseline_null = prior_factor("beta", parameters = list(alpha = 1, beta = 1),
contrast = "independent"),
models = FALSE,
silent = FALSE,
...
)check_setup.reg invisibly returns list of summary tables.
list of prior distributions for the effect size (mu)
parameter that will be treated as belonging to the alternative hypothesis. Defaults to
prior(distribution = "student", parameters = list(location = 0, scale = 0.58, df = 4)),
based on logOR meta-analytic estimates from the Cochrane Database of Systematic Reviews
bartos2023empiricalRoBMA.
list of prior distributions for the heterogeneity tau
parameter that will be treated as belonging to the alternative hypothesis. Defaults to
prior(distribution = "invgamma", parameters = list(shape = 1.77, scale = 0.55)) that
is based on heterogeneities of logOR estimates from the Cochrane Database of Systematic Reviews
bartos2023empiricalRoBMA.
list of prior distributions for the effect size (mu)
parameter that will be treated as belonging to the null hypothesis. Defaults to
a point null hypotheses at zero,
prior(distribution = "point", parameters = list(location = 0)).
list of prior distributions for the heterogeneity tau
parameter that will be treated as belonging to the null hypothesis. Defaults to
a point null hypotheses at zero (a fixed effect meta-analytic models),
prior(distribution = "point", parameters = list(location = 0)).
prior distributions for the alternative hypothesis about
intercepts (pi) of each study. Defaults to NULL.
prior distributions for the null hypothesis about
intercepts (pi) for each study. Defaults to an independent uniform prior distribution
for each intercept prior("beta", parameters = list(alpha = 1, beta = 1), contrast = "independent").
should the models' details be printed.
whether all print messages regarding the fitting process
should be suppressed. Defaults to TRUE. Note that parallel = TRUE
also suppresses all messages.
additional arguments.
check_setup() BiBMA()