RprobitB_fit: Create object of class 'RprobitB_fit'
In RprobitB: Bayesian Probit Choice Modeling

RprobitB_fit

R Documentation

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'
print(x, ...)

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

## S3 method for class 'summary.RprobitB_fit'
print(x, digits = 2, ...)

Arguments

`data`	An object of class `RprobitB_data`.
`scale`	[`character(1)`] 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`	[`integer(1)`] The number of iterations of the Gibbs sampler.
`B`	[`integer(1)`] The length of the burn-in period.
`Q`	[`integer(1)`] The thinning factor for the Gibbs samples.
`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.
`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.
`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.