R/rfyzstarcens2.R

In BNPdensity: Ferguson-Klass Type Algorithm for Posterior Normalized Random Measures

#' Conditional posterior distribution of the distinct vectors (Ystar,Zstar) in
#' the case of censoring
#'
#' This function evaluates the ratio of conditional posterior distributions of
#' the distinct latent vectors (Ystar,Zstar).
#'
#' For internal use
#'
#' @keywords internal
#' @examples
#'
#' ## The function is currently defined as
#' function(v, v2, z, z2, xleft, xright, censor_code, distr.k,
#'          distr.py0, mu.py0, sigma.py0, distr.pz0, mu.pz0, sigma.pz0) {
#'   alpha <- p0(v, distr = distr.py0, mu = mu.py0, sigma = sigma.py0) / p0(v2,
#'     distr = distr.py0, mu = mu.py0, sigma = sigma.py0
#'   ) *
#'     p0(z, distr = distr.pz0, mu = mu.pz0, sigma = sigma.pz0) / p0(z2,
#'       distr = distr.pz0, mu = mu.pz0, sigma = sigma.pz0
#'     )
#'   Prod <- 1
#'   for (i in seq_along(xleft)) {
#'     fac <- dkcens2_1val(
#'       xleft = xleft[i], xright = xright[i],
#'       c_code = censor_code[i], distr = distr.k, mu = v,
#'       sigma = z
#'     ) / dkcens2_1val(
#'       xleft = xleft[i], xright = xright[i],
#'       c_code = censor_code[i], distr = distr.k, mu = v2,
#'       sigma = z2
#'     )
#'     Prod <- Prod * fac
#'   }
#'   f <- alpha * Prod
#'   return(f)
#' }
rfyzstarcens2 <-
  function(v, v2, z, z2, xleft, xright, censor_code, distr.k,
           distr.py0, mu.py0, sigma.py0, distr.pz0, mu.pz0, sigma.pz0) {
    alpha <- p0(v, distr = distr.py0, mu = mu.py0, sigma = sigma.py0) / p0(v2,
      distr = distr.py0, mu = mu.py0, sigma = sigma.py0
    ) *
      p0(z, distr = distr.pz0, mu = mu.pz0, sigma = sigma.pz0) / p0(z2,
        distr = distr.pz0, mu = mu.pz0, sigma = sigma.pz0
      )
    Prod <- 1
    for (i in seq_along(xleft)) {
      fac <- dkcens2_1val(
        xleft = xleft[i], xright = xright[i],
        c_code = censor_code[i], distr = distr.k, mu = v,
        sigma = z
      ) / dkcens2_1val(
        xleft = xleft[i], xright = xright[i],
        c_code = censor_code[i], distr = distr.k, mu = v2,
        sigma = z2
      )
      Prod <- Prod * fac
    }
    f <- alpha * Prod
    return(f)
  }