v01_model_definition.R
In RprobitB: Bayesian Probit Choice Modeling

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "img/",
  fig.align = "center",
  fig.dim = c(8, 6), 
  out.width = "75%"
)
library("RprobitB")
options("RprobitB_progress" = FALSE)

## ---- RprobitB-parameter-example----------------------------------------------
RprobitB:::RprobitB_parameter(
  P_f   = 1,           
  P_r   = 2,
  J     = 3,
  N     = 10,
  C     = 2,          # the number of latent classes
  alpha = c(1),       # the fixed coefficient vector of length 'P_f'
  s     = c(0.6,0.4), # the vector of class weights of length 'C'
  b     = matrix(c(-1,1,1,2), nrow = 2, ncol = 2),           
                      # the matrix of class means as columns of dimension 'P_r' x 'C'
  Omega = matrix(c(diag(2),0.1*diag(2)), nrow = 4, ncol = 2),       
                      # the matrix of class covariance matrices as columns of dimension 'P_r^2' x 'C'
  Sigma = diag(2),    # the differenced error term covariance matrix of dimension '(J-1)' x '(J-1)'
                      # the undifferenced error term covariance matrix is labeled 'Sigma_full'
  z     = rep(1:2,5)  # the vector of the allocation variables of length 'N'
)