R/matern.image.cov.R

In fields: Tools for Spatial Data

#
# fields  is a package for analysis of spatial data written for
# the R software environment.
# Copyright (C) 2022 Colorado School of Mines
# 1500 Illinois St., Golden, CO 80401
# Contact: Douglas Nychka,  douglasnychka@gmail.edu,
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with the R software environment if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
# or see http://www.r-project.org/Licenses/GPL-2
##END HEADER
matern.image.cov <- function(ind1, ind2, Y, cov.obj = NULL, 
    setup = FALSE, grid, M = NULL, N = NULL,
    aRange=1, smoothness=.5,theta=NULL ) {
    # theta argument has been depreciated.
    if( !is.null( theta)){
        aRange<- theta
    }
    #
    if (is.null(cov.obj)) {
        dx <- grid$x[2] - grid$x[1]
        dy <- grid$y[2] - grid$y[1]
        m <- length(grid$x)
        n <- length(grid$y)
        if (is.null(M)) 
            M <- ceiling2(2 * m)
        if (is.null(N)) 
            N <- ceiling2(2 * n)
# make sure M and N are even.
# (not sure what it means if this is not the case!)        
        if( M%%2 !=0) {
              M<- M+1}
        if( N%%2 !=0) {
              N<- N+1}    
# need to evaluate the covariance between the center of the grid and
# every grid point do this using several simple steps for efficiency.
        xGrid<- (1:M) * dx - (dx * M)/2
        yGrid<- (1:N) * dy -  (dy * N)/2
# a matrix the same size as the grid that has the distance between every
# grid point and the center point. 
        bigDistance<-
           sqrt(
             matrix( xGrid^2, M,N, byrow=FALSE) +
             matrix( yGrid^2, M,N, byrow=TRUE) )
# this should make for a nice image plot of the covariance w/r to the center point #       
        out<- Matern( bigDistance /aRange, smoothness=smoothness)
        temp <- matrix(0, nrow = M, ncol = N)
        temp[M/2, N/2] <- 1
        wght <- fft(out)/(fft(temp) * M * N)
        cov.obj <- list(m = m, n = n, grid = grid, N = N, M = M, 
            wght = wght, call = match.call())
        if (setup) {
            return(cov.obj)
        }
    }
    temp <- matrix(0, nrow = cov.obj$M, ncol = cov.obj$N)
    if (missing(ind1)) {
        temp[1:cov.obj$m, 1:cov.obj$n] <- Y
        Re(fft(fft(temp) * cov.obj$wght, inverse = TRUE)[1:cov.obj$m, 
            1:cov.obj$n])
    }
    else {
        if (missing(ind2)) {
            temp[ind1] <- Y
        }
        else {
            temp[ind2] <- Y
        }
        Re(fft(fft(temp) * cov.obj$wght, inverse = TRUE)[ind1])
    }
}