R/LKDist.R

In LatticeKrig: Multi-Resolution Kriging Based on Markov Random Fields

# LatticeKrig  is a package for analysis of spatial data written for
# the R software environment .
# Copyright (C) 2016
# University Corporation for Atmospheric Research (UCAR)
# Contact: Douglas Nychka, nychka@ucar.edu,
# National Center for Atmospheric Research, PO Box 3000, Boulder, CO 80307-3000
#
# 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

LKDist<- function(  x1, x2, delta, max.points = NULL, 
    mean.neighbor = 50, distance.type="Euclidean")
    {
# find distances between x1 and x2 but only for those pairs within
# a distance delta
#
# if distance on sphere create 3-d coordinates   
# NOTE: chordal and great circle are monotonically related
#       by    GC= theta*R  CD = 2*R*sin( theta/2)
#               theta is angle of separation in radians
#
   if( (distance.type=="Chordal") | ( distance.type=="GreatCircle") ){
   	 if( !is.null( attr(distance.type, "Radius"))){
   	 	R<- attr(distance.type, "Radius")
   	 	}
   	 	else{
   	    R<- 3963.34
   	 	}
   	 x1<- directionCosines(x1)*R
   	 x2<- directionCosines(x2)*R
   	 if( distance.type=="GreatCircle" ){
# inflate  delta cutoff to reflect chordal distance 
     deltaGC<- delta
     delta<- 2*R*sin(  deltaGC/ (2*R)) 
     }
   }
# figure out how large an array to allocate for distance matrix   
    n1 <- nrow(x1)
    n2 <- nrow(x2)
    dimension<- ncol(x1)
    if (is.null(max.points)) {
        Nmax <- max(c(n1,n2)) * mean.neighbor
    }
    else {
        Nmax <- max.points
    }
 #  subroutine lkdist( x1, n1, x2, n2, dim,  delta2, ind, rd, Nmax, iflag)   
        out <- .Fortran("lkdist",
                        x1 = as.double(x1),
                        n1 = as.integer(n1), 
                        x2 = as.double(x2),
                        n2 = as.integer(n2),
                            dim = as.integer(dimension), 
                         delta2 = as.double(delta^2),
                            ind = as.integer(rep(0, Nmax * 2)),
                             rd = as.double(rep(-1, Nmax)),
                           Nmax = as.integer(Nmax),
                          iflag = as.integer(1),
                                PACKAGE = "LatticeKrig")
# negative iflag means one has run out of space
    if (out$iflag == -1) {
        cat("temp space set at", Nmax, fill = TRUE)
        stop("Ran out of space, increase the max.points")
    }
# trim down to a sparse matrix object where the elements  have
# nonzero values (there are out$Nmax of these)
    N <- out$Nmax
# Note output distance matrix is in "spind" sparse matrix format:
    out<- list(ind = matrix(out$ind, nrow=Nmax, ncol=2)[1:N, ],
                ra = out$rd[1:N],
                da = c(n1, n2))
    # convert chordal distance to great circle            
    if(distance.type=="GreatCircle" ){
    out$ra<-  2*R* asin( out$ra/(2*R))
    }            
    return(out)
 }