Overview on Priors for brms Models

Get information on all parameters (and parameter classes) for which priors may be specified including default priors.

get_prior(formula, data = NULL, family = gaussian(), autocor = NULL,
  partial = NULL, threshold = c("flexible", "equidistant"),
  internal = FALSE)
An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.
An optional data frame, list or environment (or object coercible by to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically t
A description of the error 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. Currently, the following families are supported: gaussian
An optional cor_brms object describing the correlation structure within the response variable (i.e. the 'autocorrelation'). See the documentation of cor_brms
A one sided formula of the form ~expression allowing to specify predictors with category specific effects in non-cumulative ordinal models (i.e. in families cratio, sratio, or acat).
A character string indicating the type of thresholds (i.e. intercepts) used in an ordinal model. "flexible" provides the standard unstructured thresholds and "equidistant" restricts the distance between consecutive thresh
A flag indicating if the names of additional internal parameters should be displayed. Setting priors on these parameters is not recommended

  • A data.frame with columns prior, class, coef, and group and several rows, each providing information on a paramter (or parameter class) on which priors can be specified. The prior column is empty except for internal default priors.

See Also


  • get_prior
## get all parameters and parameters classes to define priors on
(prior <- get_prior(count ~ log_Age_c + log_Base4_c * Trt_c
                    + (1|patient) + (1|visit),
                    data = epilepsy, family = poisson()))   
## define a prior on all fixed effects a once
prior$prior[1] <- "normal(0,10)"

## define a specific prior on the fixed effect of Trt_c
prior$prior[5] <- "student_t(10, 0, 5)"       

## verify that the priors indeed found their way into Stan's model code
make_stancode(count ~ log_Age_c + log_Base4_c * Trt_c 
              + (1|patient) + (1|visit),
              data = epilepsy, family = poisson(), 
              prior = prior)
Documentation reproduced from package brms, version 0.7.0, License: GPL (>= 3)

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