R/gaussian.R

In nimblewomble: Bayesian Wombling using 'nimble'

#' Squared Exponential Covariance kernel
#'
#' Computes the Matern covariance matrix with fractal parameter
#' \eqn{\nu \to \infty}.
#'
#' Has the option to compute \eqn{\Sigma_{d\times d} + \tau^2 I_d}.
#'
#' @param dists distance matrix
#' @param phi spatial range
#' @param sigma2 spatial variance
#' @param tau2 nugget variance
#' @returns A matrix of covariance terms. For internal use only.
#' @keywords materncov2
#' @importFrom nimble nimbleFunction nimDim nimMatrix
#' @examples
#' \donttest{
#' #####################
#' # Internal use only #
#' #####################
#' # Used across multiple functions
#' # Example usage
#' require(nimble)
#' require(nimblewomble)
#'
#' set.seed(1)
#' # Generated Simulated Data
#' N = 1e2
#' tau = 1
#' coords = matrix(runif(2 * N, -10, 10), ncol = 2)
#' colnames(coords) = c("x", "y")
#' dists = as.matrix(dist(coords))
#'
#' cGaussian  = compileNimble(gaussian)
#' cGaussian(dists = dists[1:N, 1:N], phi = 1, sigma2 = 1, tau2 = 0)
#' }
#' @author Aritra Halder <aritra.halder@drexel.edu>, \cr
#' Sudipto Banerjee <sudipto@ucla.edu>
#' @export
gaussian <- nimble::nimbleFunction(
  run = function(dists = double(2),
                 phi = double(0),
                 sigma2 = double(0),
                 tau2 = double(0)){

    returnType(double(2))
    n <- dim(dists)[1]

    result <- matrix(nrow = n, ncol = n, init = FALSE)

    # result[1:n, 1:n] <- sigma2 * exp(-phi^2 * dists[1:n, 1:n]^2) + tau2 * diag(n)

    for(i in 1:n)
      for(j in 1:n){
        result[i, j] <- sigma2 *  exp(-phi^2 * dists[i, j]^2)
        if(i == j)  result[i, j] <- result[i, j] + tau2
        else result[i, j] <- result[i, j]
      }

    return(result)
  })