Define custom prior distributions for epidist models

This function combines model specific prior distributions from epidist_model_prior(), family specific prior distributions from epidist_family_prior(), and user provided prior distributions into a single set of custom priors. Each element overwrites previous elements, such that user provided prior distributions have the highest priority. If a user prior distribution is provided which is not included in the model, a warning will be shown.

Usage

epidist_prior(
  data,
  family,
  formula,
  prior,
  merge = TRUE,
  enforce_presence = FALSE
)

Arguments

data

An object with class corresponding to an implemented model.

family

A description of the response distribution and link function to be used in the model created using epidist_family().

formula

A symbolic description of the model to be fitted created using epidist_formula().

prior

One or more brmsprior objects created by brms::set_prior() or related functions. These priors are passed to epidist_prior() in the prior argument. Some models have default priors that are automatically added (see epidist_model_prior()). These can be merged with user-provided priors using the merge_priors argument.

merge

If TRUE then merge new priors with existing ones, if FALSE only use new priors. Defaults to TRUE. This may be useful if the built in approaches for merging priors are not flexible enough for a particular use case.

enforce_presence

If TRUE then only allow user priors that match existing default priors. If FALSE then allow user priors that are not present in the default set. Defaults to FALSE.

Value

A brmsprior object containing the combined custom prior distributions.

Details

Note that the matching of priors is imperfect as it does not use brms' internal prior matching functionality. For example, it cannot distinguish between a prior for all coefficients (class = "b") and a prior for a specific coefficient (class = "b" and coef specified).

See also

Other prior: epidist_family_prior(), epidist_family_prior.default(), epidist_family_prior.lognormal(), epidist_model_prior(), epidist_model_prior.default()