Validate Prior for brms Models

Validate priors supplied by the user. Return a complete set of priors for the given model, including default priors.

validate_prior(
  prior,
  formula,
  data,
  family = gaussian(),
  sample_prior = "no",
  data2 = NULL,
  knots = NULL,
  drop_unused_levels = TRUE,
  ...
)

Arguments

prior: One or more brmsprior objects created by set_prior or related functions and combined using the c method or the + operator. See also default_prior for more help.
formula: An object of class formula, brmsformula, or mvbrmsformula (or one that can be coerced to that classes): A symbolic description of the model to be fitted. The details of model specification are explained in brmsformula.
data: An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model.
family: A description of the response distribution and link function to be used in the model. This can be a family function, a call to a family function or a character string naming the family. Every family function has a link argument allowing to specify the link function to be applied on the response variable. If not specified, default links are used. For details of supported families see brmsfamily. By default, a linear gaussian model is applied. In multivariate models, family might also be a list of families.
sample_prior: Indicate if draws from priors should be drawn additionally to the posterior draws. Options are "no" (the default), "yes", and "only". Among others, these draws can be used to calculate Bayes factors for point hypotheses via hypothesis. Please note that improper priors are not sampled, including the default improper priors used by brm. See set_prior on how to set (proper) priors. Please also note that prior draws for the overall intercept are not obtained by default for technical reasons. See brmsformula how to obtain prior draws for the intercept. If sample_prior is set to "only", draws are drawn solely from the priors ignoring the likelihood, which allows among others to generate draws from the prior predictive distribution. In this case, all parameters must have proper priors.
data2: A named list of objects containing data, which cannot be passed via argument data. Required for some objects used in autocorrelation structures to specify dependency structures as well as for within-group covariance matrices.
knots: Optional list containing user specified knot values to be used for basis construction of smoothing terms. See gamm for more details.
drop_unused_levels: Should unused factors levels in the data be dropped? Defaults to TRUE.
...: Other arguments for internal usage only.

Value

An object of class brmsprior.

Examples

prior1 <- prior(normal(0,10), class = b) +
  prior(cauchy(0,2), class = sd)
validate_prior(prior1, count ~ zAge + zBase * Trt + (1|patient),
               data = epilepsy, family = poisson())
#>                   prior     class       coef   group resp dpar nlpar lb ub
#>  student_t(3, 1.4, 2.5) Intercept                                         
#>           normal(0, 10)         b                                         
#>           normal(0, 10)         b       Trt1                              
#>           normal(0, 10)         b       zAge                              
#>           normal(0, 10)         b      zBase                              
#>           normal(0, 10)         b zBase:Trt1                              
#>            cauchy(0, 2)        sd                                     0   
#>            cauchy(0, 2)        sd            patient                  0   
#>            cauchy(0, 2)        sd  Intercept patient                  0   
#>        source
#>       default
#>          user
#>  (vectorized)
#>  (vectorized)
#>  (vectorized)
#>  (vectorized)
#>          user
#>  (vectorized)
#>  (vectorized)

Arguments

Value

See also

Examples