R/rlambar.xn.R

In eliardocosta/ssdet: Sample size determination in frequentist and Bayesian approaches

#' Random generation for the posterior functional mean of the Poisson/Dirichlet process with a gamma distribution as the $F_0$ base distribution.
#'
#' @param N Number of observations.
#' @param alpha Shape parameter of the Dirichlet process.
#' @param x Observed counts of the Poisson distribution of the model.
#' @param w A positive real number representing the aliquot volume.
#' @param lam0 A positive real number representing a hyperparameter of the $F_0$ base distribution. 
#' @param theta0 A positive real number representing a hyperparameter of the $F_0$ base distribution. We consider $F_0$ as the gamma distribution with mean $lam_0$ and shape parameter $theta_0$.
#' @param ncore Number of cores to use in parallel computin. If NULL the function uses 1 core if there is only one core, if there is more than one cores uses one half of the cores.
#'
#' @return A random sample of the posterior functional mean of the Poisson/Dirichlet process.
#' @export
#'
rlambar.xn <- function(N, alpha, x, w, lam0, theta0, ncore) {
  Z <- rnu(nsam = N, x = x, w = w, lam0 = lam0, theta0 = theta0, alpha = alpha)
  D <- rdiri(N = N, k = length(x))
  lambar <- rlambar(N = N, alpha = alpha, lam0 = lam0, theta0 = theta0, ncore = ncore)
  B <- stats::rbeta(N, alpha, length(x))
  output <- B*lambar + (1 - B)*apply(D*Z, 1, sum)
  return(output)
}