configure_model: Generic S3 method for configuring the model to be fit by brms

In bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

Generic S3 method for configuring the model to be fit by brms

Description

Called by bmm() to automatically construct the model formula, family objects and default priors for the model specified by the user. It will call the appropriate configure_model.* functions based on the list of classes defined in the .model_* functions. Currently, we have a method only for the last class listed in the .model_* functions. This is to keep model configuration as simple as possible. In the future we may add shared methods for classes of models that share the same configuration.

Usage

configure_model(model, data, formula)

Arguments

model	A model list object returned from check_model()
data	The user supplied data.frame containing the data to be checked
formula	The user supplied formula

Details

A bare bones configure_model.* method should look like this:

configure_model.newmodel <- function(model, data, formula) {

   # preprocessing - e.g. extract arguments from data check, construct new variables
   <preprocessing code>

   # construct the formula
   formula <- bmf2bf(formula, model)

   # construct the family
   family <- <code for new family>

   # construct the default prior
   prior <- <code for new prior>

   # return the list
   nlist(formula, data, family, prior)
}

Value

A named list containing at minimum the following elements:

• formula: An object of class brmsformula. The constructed model formula

• data: the user supplied data.frame, preprocessed by check_data

• family: the brms family object

• prior: the brms prior object

• stanvars: (optional) An object of class stanvars (for custom families). See brms::custom_family() for more details.

Examples

configure_model.mixture3p <- function(model, data, formula) {
  # retrieve arguments from the data check
  max_set_size <- attr(data, "max_set_size")
  lure_idx <- attr(data, "lure_idx_vars")
  nt_features <- model$other_vars$nt_features
  set_size_var <- model$other_vars$set_size

  # construct initial brms formula
  formula <- bmf2bf(model, formula) +
    brms::lf(kappa2 ~ 1) +
    brms::lf(mu2 ~ 1) +
    brms::nlf(theta1 ~ thetat) +
    brms::nlf(kappa1 ~ kappa)

  # additional internal terms for the mixture model formula
  kappa_nts <- paste0("kappa", 3:(max_set_size + 1))
  theta_nts <- paste0("theta", 3:(max_set_size + 1))
  mu_nts <- paste0("mu", 3:(max_set_size + 1))

  for (i in 1:(max_set_size - 1)) {
    formula <- formula +
      glue_nlf("{kappa_nts[i]} ~ kappa") +
      glue_nlf(
        "{theta_nts[i]} ~ {lure_idx[i]} * (thetant + log(inv_ss)) + ",
        "(1 - {lure_idx[i]}) * (-100)"
      ) +
      glue_nlf("{mu_nts[i]} ~ {nt_features[i]}")
  }

  # define mixture family
  vm_list <- lapply(1:(max_set_size + 1), function(x) brms::von_mises(link = "identity"))
  vm_list$order <- "none"
  formula$family <- brms::do_call(brms::mixture, vm_list)

  nlist(formula, data)
}

bmm documentation built on May 29, 2024, 11:52 a.m.