R/fcondYZXA.R

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

#' Conditional posterior distribution of the bivariate latents (Y,Z)
#'
#' This function simulates form the conditional posterior distribution of the
#' latents (Y,Z).
#'
#' For internal use.
#'
#' @keywords internal
#' @examples
#'
#' ## The function is currently defined as
#' function(x, distr = 1, Tauy, Tauz, J) {
#'   K <- matrix(NA, nrow = length(Tauy), ncol = length(x))
#'   for (i in seq(Tauy)) {
#'     K[i, ] <- dk(x, distr = distr, mu = Tauy[i], sigma = Tauz[i]) *
#'       J[i]
#'   }
#'   if (any(is.na(K))) {
#'     print(K, Tauy, Tauz, J)
#'   }
#'   pK <- prop.table(K, margin = 2)
#'   j <- apply(pK, 2, function(x) {
#'     sample(length(Tauy),
#'       size = 1,
#'       prob = x
#'     )
#'   })
#'   return(matrix(c(y = Tauy[j], z = Tauz[j]),
#'     nrow = length(x),
#'     ncol = 2
#'   ))
#' }
fcondYZXA <-
  function(x, distr, Tauy, Tauz, J) {
    K <- matrix(NA, nrow = length(Tauy), ncol = length(x))
    for (i in seq(Tauy)) {
      K[i, ] <- dk(x, distr = distr, mu = Tauy[i], sigma = Tauz[i]) *
        J[i]
    }
    if (any(is.na(K))) {
      print(K, Tauy, Tauz, J)
    }
    pK <- prop.table(K, margin = 2)
    j <- apply(pK, 2, function(x) {
      sample(length(Tauy),
        size = 1,
        prob = x
      )
    })
    return(matrix(c(y = Tauy[j], z = Tauz[j]),
      nrow = length(x),
      ncol = 2
    ))
  }