check_setup  R Documentation 
Prints summary of "RoBTT"
ensemble implied by the specified priors
Description
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.
Usage
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()