Fit a Bayesian Structural Choice Discounting Model via brms

The TMB structural likelihood of fit_dd_choice(): logit P(LL) = [b0] + gamma * ((ll/ss) * D(k, delay) - 1) with choice sensitivity gamma = exp(loggamma) and the discount rate k = exp(logk) carrying the subject random intercept. With bernoulli("logit") the brms nonlinear formula IS the logit, so the likelihood matches TMB exactly.

Usage

fit_dd_choice_brms(
  data,
  mode = c("structural", "descriptive"),
  id_var = "id",
  ss_var = "ss_amount",
  ll_var = "ll_amount",
  delay_var = "delay",
  choice_var = "choice",
  equation = c("mazur", "exponential"),
  intercept = FALSE,
  factors = NULL,
  factor_interaction = FALSE,
  continuous_covariates = NULL,
  prior = NULL,
  autoscale_priors = TRUE,
  chains = 4,
  iter = 2000,
  warmup = floor(iter/2),
  thin = 1,
  cores = getOption("mc.cores", 1L),
  seed = NA,
  backend = getOption("brms.backend", "rstan"),
  control = list(adapt_delta = 0.95),
  init = c("prior_center", "tmb", "random", "0"),
  sample_prior = "no",
  loo = TRUE,
  file = NULL,
  file_refit = getOption("brms.file_refit", "on_change"),
  verbose = 1,
  ...
)

Arguments

data: Long-format trial-level data (one row per choice).
mode: "structural" (only mode in v1).
id_var, ss_var, ll_var, delay_var, choice_var: Column names, as in fit_dd_choice().
equation: "mazur" or "exponential".
intercept: Include the logit-scale bias term b0.
factors, factor_interaction, continuous_covariates: Between-subject fixed-effect design on log k (same semantics as fit_dd_choice()); gamma and b0 stay population-level.
prior: Optional brmsprior; user rows override the defaults.
autoscale_priors: Anchor the logk prior to the median delay (see default_dd_priors()).
chains, iter, warmup, thin, cores, seed, backend, control, sample_prior: MCMC settings passed to brms::brm().
init: "prior_center" (default), "tmb" (a quiet fit_dd_tmb() pre-fit supplies the centers, with prior_center fallback on failure; the beta family maps to the TMB sltb pre-fit), "random", or "0"; or a list/function passed through to brms::brm().
loo: Compute and store brms::loo() at fit time.
file, file_refit: Passed to brms::brm() for fit caching.
verbose: 0 (silent), 1 (messages), 2 (full Stan output).
...: Passed through to brms::brm().

Value

An object of class beezdiscounting_choice_brms. Coefficients are posterior medians on the estimation scale under the TMB names (one beta_k per design column, log_gamma, and beta0 when intercept = TRUE).

Details

v1 implements mode = "structural" only: the descriptive (Young 2018) model is a plain logistic GLMM expressible directly with brms::brm().

Usage

Arguments

Value

Details

See also