RprobitB_fitThis function creates an object of class RprobitB_fit.
RprobitB_fit(
data,
scale,
level,
normalization,
R,
B,
Q,
latent_classes,
prior,
gibbs_samples,
class_sequence,
comp_time
)# S3 method for RprobitB_fit
print(x, ...)
# S3 method for RprobitB_fit
summary(object, FUN = c(mean = mean, sd = stats::sd, `R^` = R_hat), ...)
# S3 method for summary.RprobitB_fit
print(x, digits = 2, ...)
An object of class RprobitB_fit.
An object of class RprobitB_data.
[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.
An object of class RprobitB_normalization.
[integer(1)]
The number of iterations of the Gibbs sampler.
[integer(1)]
The length of the burn-in period.
[integer(1)]
The thinning factor for the Gibbs samples.
[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.
[list]
A named list of parameters for the prior distributions. See the documentation
of check_prior for details about which parameters can be
specified.
An object of class RprobitB_gibbs_samples.
The sequence of class numbers during Gibbs sampling of length R.
The time spent for Gibbs sampling.