RprobitB_model: Create object of class 'RprobitB_model'.

In RprobitB: Bayes Estimation of Latent Class Mixed Multinomial Probit Models

Description

This function creates an object of class RprobitB_model.

Usage

RprobitB_model(
  data,
  normalization,
  R,
  B,
  Q,
  latent_classes,
  prior,
  gibbs_samples,
  classification
)

Arguments

data	An object of class RprobitB_data.
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 or a list of parameters specifying the number and the latent classes: C: The number (greater or equal 1) of latent classes, which is set to 1 per default and is ignored if P_r = 0. If update = TRUE, C equals the initial number of classes. update: A boolean, determining whether to update C. Ignored if P_r = 0. If update = FALSE, all of the following elements are ignored. Cmax: The maximum number of latent classes. buffer: The updating buffer (number of iterations to wait before the next update). epsmin: The threshold weight for removing latent classes (between 0 and 1). epsmax: The threshold weight for splitting latent classes (between 0 and 1). distmin: The threshold difference in means for joining latent classes (non-negative).
prior	A named list of parameters for the prior distributions of the normalized parameters: eta: The mean vector of length P_f of the normal prior for alpha. Psi: The covariance matrix of dimension P_f x P_f of the normal prior for alpha. delta: The concentration parameter of length 1 of the Dirichlet prior for s. xi: The mean vector of length P_r of the normal prior for each b_c. D: The covariance matrix of dimension P_r x P_r of the normal prior for each b_c. nu: The degrees of freedom (a natural number greater than P_r) of the Inverse Wishart prior for each Omega_c. Theta: The scale matrix of dimension P_r x P_r of the Inverse Wishart prior for each Omega_c. kappa: The degrees of freedom (a natural number greater than J-1) of the Inverse Wishart prior for Sigma. E: The scale matrix of dimension J-1 x J-1 of the Inverse Wishart prior for Sigma.
gibbs_samples	An object of class RprobitB_gibbs_samples.
classification	The allocation variable of the estimated latent classes.

Value

An object of class RprobitB_model, i.e. a list with the arguments of this function as elements.

RprobitB documentation built on Nov. 12, 2021, 5:08 p.m.