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Create object of class RprobitB_fit

Source: R/model_fitting.R
RprobitB_fit.Rd

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 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.