`"RoBTT"`

ensemble implied by the specified priors`R/check-input-and-settings.R`

`check_setup.Rd`

`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
)
```

- 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.

`check_setup`

invisibly returns list of summary tables.