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

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

check_setup(
  prior_delta = prior(distribution = "cauchy", parameters = list(location = 0, scale =
    sqrt(2)/2)),
  prior_rho = prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)),
  prior_nu = prior(distribution = "exp", parameters = list(rate = 1)),
  prior_delta_null = prior(distribution = "spike", parameters = list(location = 0)),
  prior_rho_null = prior(distribution = "spike", parameters = list(location = 0.5)),
  prior_nu_null = prior_none(),
  prior_mu = NULL,
  prior_sigma2 = NULL,
  truncation = NULL,
  models = FALSE,
  silent = FALSE
)

Arguments

prior_delta

prior distributions for the effect size delta parameter that will be treated as belonging to the alternative hypothesis. Defaults to prior(distribution = "Cauchy", parameters = list(location = 0, scale = sqrt(2)/2)).

prior_rho

prior distributions for the precision allocation rho parameter that will be treated as belonging to the alternative hypothesis. Defaults to prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

prior_nu

prior distribution for the degrees of freedom + 2 nu parameter that will be treated as belonging to the alternative hypothesis. Defaults to prior(distribution = "exp", parameters = list(rate = 1)) if no truncation is specified. If truncation is specified, the default is NULL (i.e., use only normal likelihood).

prior_delta_null

prior distribution for the delta parameter that will be treated as belonging to the null hypothesis. Defaults to point distribution with location at 0 ( prior(distribution = "point", parameters = list(location = 0))).

prior_rho_null

prior distribution for the rho parameter that will be treated as belonging to the null hypothesis. Defaults to point distribution with location at 0.5 ( prior(distribution = "point", parameters = list(location = 0.5))).

prior_nu_null

prior distribution for the nu parameter that will be treated as belonging to the null hypothesis. Defaults to prior_none ( (i.e., normal likelihood)).

prior_mu

prior distribution for the grand mean parameter. Defaults to NULL which sets Jeffreys prior for the grand mean in case of no truncation or an unit Cauchy prior distributions for the grand mean in case of truncation (which greatly improves sampling efficiency).

prior_sigma2

prior distribution for the grand variance parameter. Defaults to NULL which sets Jeffreys prior for the variance in case of no truncation or an exponential prior distribution for the variance in case of truncation (which greatly improves sampling efficiency).

truncation

an optional list specifying truncation applied to the data. Defaults to NULL, i.e., no truncation was applied and the full likelihood is applied. Alternative the truncation can be specified via a named list with:

"x"

where x is a vector of two values specifying the lower and upper truncation points common across the groups

"x1" and "x2"

where x1 is a vector of two values specifying the lower and upper truncation points for the first group and x2 is a vector of two values specifying the lower and upper truncation points for the second group.

"sigma"

where sigma corresponds to the number of standard deviations from the common mean where the truncation points should be set.

"sigma1" and "sigma2"

where sigma1 corresponds to the number of standard deviations from the mean of the first group where the truncation points should be set and sigma2 corresponds to the number of standard deviations from the mean of the second group where the truncation points should be set.

models

should the models' details be printed.

silent

do not print the results.

Value

check_setup invisibly returns list of summary tables.

See also

RoBTT()