RprobitB_fit: Create object of class 'RprobitB_fit'

In RprobitB: Bayesian Probit Choice Modeling

Create object of class RprobitB_fit

Description

This function creates an object of class RprobitB_fit.

Usage

RprobitB_fit(
  data,
  scale,
  level,
  normalization,
  R,
  B,
  Q,
  latent_classes,
  prior,
  gibbs_samples,
  class_sequence,
  comp_time
)

## S3 method for class 'RprobitB_fit'
summary(object, FUN = c(mean = mean, sd = stats::sd, `R^` = R_hat), ...)

Arguments

data	An object of class RprobitB_data.
scale	A character which determines the utility scale. It is of the form ⁠<parameter> := <value>⁠, where ⁠<parameter>⁠ is either the name of a fixed effect or ⁠Sigma_<j>,<j>⁠ for the ⁠<j>⁠th diagonal element of Sigma, and ⁠<value>⁠ is the value of the fixed parameter.
normalization	An object of class RprobitB_normalization.
R	The number of iterations of the Gibbs sampler.
B	The length of the burn-in period, i.e. a non-negative number of samples to be discarded.
Q	The thinning factor for the Gibbs samples, i.e. only every Qth sample is kept.
latent_classes	Either NULL (for no latent classes) or a list of parameters specifying the number of latent classes and their updating scheme: C: The fixed number (greater or equal 1) of latent classes, which is set to 1 per default. If either weight_update = TRUE or dp_update = TRUE (i.e. if classes are updated), C equals the initial number of latent classes. weight_update: A boolean, set to TRUE to weight-based update the latent classes. See ... for details. dp_update: A boolean, set to TRUE to update the latent classes based on a Dirichlet process. See ... for details. Cmax: The maximum number of latent classes. buffer: The number of iterations to wait before a next weight-based update of the latent classes. epsmin: The threshold weight (between 0 and 1) for removing a latent class in the weight-based updating scheme. epsmax: The threshold weight (between 0 and 1) for splitting a latent class in the weight-based updating scheme. distmin: The (non-negative) threshold in class mean difference for joining two latent classes in the weight-based updating scheme.
prior	A named list of parameters for the prior distributions. See the documentation of check_prior for details about which parameters can be specified.
gibbs_samples	An object of class RprobitB_gibbs_samples.
class_sequence	The sequence of class numbers during Gibbs sampling of length R.
comp_time	The time spent for Gibbs sampling.
...	Currently not used.

Value

An object of class RprobitB_fit.

RprobitB documentation built on May 29, 2024, 7:59 a.m.