File: predict.eco.R

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r-cran-eco 4.0-1-3
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#' Out-of-Sample Posterior Prediction under the Parametric Bayesian Model for
#' Ecological Inference in 2x2 Tables
#' 
#' Obtains out-of-sample posterior predictions under the fitted parametric
#' Bayesian model for ecological inference. \code{predict} method for class
#' \code{eco} and \code{ecoX}.
#' 
#' The posterior predictive values are computed using the Monte Carlo sample
#' stored in the \code{eco} output (or other sample if \code{newdraw} is
#' specified). Given each Monte Carlo sample of the parameters, we sample the
#' vector-valued latent variable from the appropriate multivariate Normal
#' distribution. Then, we apply the inverse logit transformation to obtain the
#' predictive values of proportions, \eqn{W}. The computation may be slow
#' (especially for the nonparametric model) if a large Monte Carlo sample of
#' the model parameters is used. In either case, setting \code{verbose = TRUE}
#' may be helpful in monitoring the progress of the code.
#' 
#' @aliases predict.eco
#' @param object An output object from \code{eco} or \code{ecoNP}.
#' @param newdraw An optional list containing two matrices (or three
#' dimensional arrays for the nonparametric model) of MCMC draws of \eqn{\mu}
#' and \eqn{\Sigma}. Those elements should be named as \code{mu} and
#' \code{Sigma}, respectively. The default is the original MCMC draws stored in
#' \code{object}.
#' @param subset A scalar or numerical vector specifying the row number(s) of
#' \code{mu} and \code{Sigma} in the output object from \code{eco}. If
#' specified, the posterior draws of parameters for those rows are used for
#' posterior prediction. The default is \code{NULL} where all the posterior
#' draws are used.
#' @param verbose logical. If \code{TRUE}, helpful messages along with a
#' progress report on the Monte Carlo sampling from the posterior predictive
#' distributions are printed on the screen. The default is \code{FALSE}.
#' @param ... further arguments passed to or from other methods.
#' @return \code{predict.eco} yields a matrix of class \code{predict.eco}
#' containing the Monte Carlo sample from the posterior predictive distribution
#' of inner cells of ecological tables. \code{summary.predict.eco} will
#' summarize the output, and \code{print.summary.predict.eco} will print the
#' summary.
#' @author Kosuke Imai, Department of Politics, Princeton University,
#' \email{kimai@@Princeton.Edu}, \url{http://imai.princeton.edu}; Ying Lu,
#' Center for Promoting Research Involving Innovative Statistical Methodology
#' (PRIISM), New York University \email{ying.lu@@nyu.Edu}
#' @seealso \code{eco}, \code{predict.ecoNP}
#' @keywords methods
predict.eco <- function(object, newdraw = NULL, subset = NULL,
                        verbose = FALSE, ...){

  if (is.null(newdraw) && is.null(object$mu))
    stop("Posterior draws of mu and Sigma must be supplied")
  else if (!is.null(newdraw)){
    if (is.null(newdraw$mu) && is.null(newdraw$Sigma))
      stop("Posterior draws of both mu and Sigma must be supplied.")
    object <- newdraw
  }

  mu <- coef(object, subset = subset)
  n.draws <- nrow(mu)
  p <- ncol(mu)
  Sigma <- varcov(object, subset = subset)
  
  Wstar <- matrix(NA, nrow=n.draws, ncol=p)
  tmp <- floor(n.draws/10)
  inc <- 1
  for (i in 1:n.draws) {
    Wstar[i,] <- mvrnorm(1, mu = mu[i,], Sigma = Sigma[,,i])
    if (i == inc*tmp & verbose) {
      cat("", inc*10, "percent done.\n")
      inc <- inc + 1
    }
  }
  res <- apply(Wstar, 2, invlogit)
  if (ncol(res) == 2)
    colnames(res) <- c("W1", "W2")
  else # this is called from predict.ecoX
    colnames(res) <- c("W1", "W2", "X")
  class(res) <- c("predict.eco", "matrix")
  return(res)
}