Default priors for the Bayesian (brms) choice model — default_dd_choice

loggamma ~ normal(1, 1) (choice sensitivity, typically 1-20) and the optional logit-scale bias b0 ~ normal(0, 1.5); logk defaults as in default_dd_priors() (autoscaled to the median delay when data is supplied; delay_var names the delay column).

Usage

default_dd_choice_priors(
  equation = c("mazur", "exponential"),
  intercept = FALSE,
  data = NULL,
  delay_var = "delay",
  factors = NULL,
  factor_interaction = FALSE,
  continuous_covariates = NULL,
  autoscale = !is.null(data)
)

Arguments

equation: "mazur" or "exponential".
intercept: Include the b0 prior.
data: Optional data frame used for logk autoscaling.
delay_var: Delay column name in data.
factors, factor_interaction, continuous_covariates: Fixed-effect design on logk, as passed to fit_dd_choice_brms(). When the design has non-intercept coefficients (derived through build_fixed_rhs()), a fold-change normal(0, 1) class-level coefficient prior is added; with an intercept-only design it is omitted (it would be unused, and brms warns).
autoscale: Logical; defaults to TRUE when data is supplied.

Value

A brmsprior data frame.