Prints summary of "RoBMA" ensemble implied by the specified priors

check_setup prints summary of "RoBMA" ensemble implied by the specified prior distributions. It is useful for checking the ensemble configuration prior to fitting all of the models.

check_setup(
  model_type = NULL,
  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
    scale = 0.15)),
  priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
    list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
    prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
    1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
    "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
    1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
    c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), 
    
    prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
    1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
    "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
    prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
    truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
    parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
    0)),
  priors_bias_null = prior_none(),
  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
  priors_hierarchical_null = NULL,
  models = FALSE,
  silent = FALSE
)

check_setup.RoBMA(
  model_type = NULL,
  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
    scale = 0.15)),
  priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
    list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
    prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
    1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
    "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
    1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
    c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), 
    
    prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
    1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
    "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
    prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
    truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
    parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
    0)),
  priors_bias_null = prior_none(),
  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
  priors_hierarchical_null = NULL,
  models = FALSE,
  silent = FALSE
)

Arguments

model_type

string specifying the RoBMA ensemble. Defaults to NULL. The other options are "PSMA", "PP", and "2w" which override settings passed to the priors_effect, priors_heterogeneity, priors_effect, priors_effect_null, priors_heterogeneity_null, priors_bias_null, and priors_effect. See details for more information about the different model types.

priors_effect

list of prior distributions for the effect size (mu) parameter that will be treated as belonging to the alternative hypothesis. Defaults to a standard normal distribution prior(distribution = "normal", parameters = list(mean = 0, sd = 1)).

priors_heterogeneity

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, scale = .15)) that is based on heterogeneities estimates from psychology (van Erp et al. 2017) .

priors_bias

list of prior distributions for the publication bias adjustment component that will be treated as belonging to the alternative hypothesis. Defaults to list( prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4) ), corresponding to the RoBMA-PSMA model introduce by Bartoš et al. (2022) .

priors_effect_null

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)).

priors_heterogeneity_null

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)).

priors_bias_null

list of prior weight functions for the omega parameter that will be treated as belonging to the null hypothesis. Defaults no publication bias adjustment, prior_none().

priors_hierarchical

list of prior distributions for the correlation of random effects (rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows users to fit a hierarchical (three-level) meta-analysis when study_ids are supplied. Note that this is an experimental feature and see News for more details. Defaults to a beta distribution prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

priors_hierarchical_null

list of prior distributions for the correlation of random effects (rho) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.

models

should the models' details be printed.

silent

do not print the results.

Value

check_setup invisibly returns list of summary tables.

See also

check_setup.reg() RoBMA()