R/BayesFH.R

In BayesSAE: Bayesian Analysis of Small Area Estimation

BayesFH <-
function(theta, beta, y, X, b, phi, n, betaprior, Sqsigmaprior, betatype, Sqsigmatype)
{
     m <- length(theta)
     p <- length(beta)
     theta <- c(theta, rnorm((n-1)*m))
     beta <- c(beta, rnorm((n-1)*p))
     if (Sqsigmatype == 0){
          a0 <- Sqsigmaprior[1]
          b0 <- Sqsigmaprior[2]
          Sqsigmav <- 1.0 / rgamma(n, shape=a0+m/2, rate=1)
          result <- .C("BayesFH", as.double(theta), as.double(beta), as.double(Sqsigmav),
                  as.double(y), as.double(X), as.double(b), as.double(phi), as.integer(n), 
               as.integer(m), as.integer(p), as.double(betaprior), as.double(b0),
               as.integer(betatype), as.integer(Sqsigmatype))
     }
     else{
          Sqsigmav <- 1.0 / rgamma(n, shape=m/2-1, rate=1)
          result <- .C("BayesFH", as.double(theta), as.double(beta), as.double(Sqsigmav), 
               as.double(y), as.double(X), as.double(b), as.double(phi), as.integer(n),
               as.integer(m), as.integer(p), as.double(betaprior), as.double(Sqsigmaprior), 
               as.integer(betatype), as.integer(Sqsigmatype))
     }
     result[[1]] <- array(result[[1]], c(m, n))
     result[[2]] <- array(result[[2]], c(p, n))
     MCMCsample <- list(theta = result[[1]], beta = result[[2]], sigv = result[[3]], type = "FH")
     MCMCsample
}