gibbs_sampler: Gibbs sampler for probit models
In RprobitB: Bayesian Probit Choice Modeling

gibbs_sampler

R Documentation

Gibbs sampler for probit models

Description

Gibbs sampler for probit models

Usage

gibbs_sampler(
  sufficient_statistics,
  prior,
  latent_classes,
  fixed_parameter,
  R,
  B,
  print_progress,
  ordered,
  ranked,
  save_beta_draws = FALSE
)

Arguments

`sufficient_statistics`	[`list`] The output of `sufficient_statistics`.
`prior`	[`list`] A named list of parameters for the prior distributions. See the documentation of `check_prior` for details about which parameters can be specified.
`latent_classes`	[`list()` \| `NULL`] Optionally parameters specifying the number of latent classes and their updating scheme. The values in brackets are the default. `C` (`1`): The fixed number (greater or equal 1) of (initial) classes. `wb_update` (`FALSE`): Set to `TRUE` for weight-based class updates. `dp_update` (`FALSE`): Set to `TRUE` for Dirichlet process class updates. `Cmax` (`10`): The maximum number of latent classes. The following specifications are used for the weight-based updating scheme: `buffer` (`50`): The number of iterations to wait before the next update. `epsmin` (`0.01`): The threshold weight for removing a latent class. `epsmax` (`0.7`): The threshold weight for splitting a latent class. `deltamin` (`0.1`): The minimum mean distance before merging two classes. `deltashift` (`0.5`): The scale for shifting the class means after a split.
`fixed_parameter`	[`list`] A named list with fixed parameter values for `alpha`, `C`, `s`, `b`, `Omega`, `Sigma`, `Sigma_full`, `beta`, `z`, or `d` for the simulation. See the vignette on model definition for definitions of these variables.
`R`	[`integer(1)`] The number of iterations of the Gibbs sampler.
`B`	[`integer(1)`] The length of the burn-in period.
`print_progress`	[`logical(1)`] Print the Gibbs sampler progress?
`ordered`	[`logical(1)`] If `TRUE`, the choice set `alternatives` is assumed to be ordered from worst to best.
`ranked`	[`logical(1)`] Are the alternatives ranked?
`save_beta_draws`	[`logical(1)`] Save draws for decider-specific coefficient vectors? Usually not recommended, as it requires a lot of storage space.