# fields, Tools for spatial data
# Copyright 2004-2011, Institute for Mathematics Applied Geosciences
# University Corporation for Atmospheric Research
# Licensed under the GPL -- www.gpl.org/licenses/gpl.html
vgram.matrix <- function(dat, R = 5, dx = 1, dy = 1) {
# get the data and dx dy if dat is passed as a list   
  if( is.list(dat)){
      if( is.null(dat$z)){
        stop("Need z component")
      dat<- dat$z
      dx<- dat$x[2]- dat$x[1]
      dy<- dat$y[2] - dat$y[1]
      R<- R*(sqrt(dx**2 + dy**2))}}
# useful function for matching shifted indices
  SI <- function(ntemp, delta) {
        n1 <- 1:ntemp
        n2 <- n1 + delta
        good <- (n2 >= 1) & (n2 <= ntemp)
        cbind(n1[good], n2[good])
    }
    # create all possible separations for a grid up to a distance R
    N <- ncol(dat)
    M <- nrow(dat)
    m <- min(c(round(R/dx), M))
    n <- min(c(round(R/dy), N))
    #
    # all relavent combinations:  note that negative increments are
    # needed as well as  positive ones
    ind <- rbind(as.matrix(expand.grid(0, 1:n)), as.matrix(expand.grid(1:m, 
        0)), as.matrix(expand.grid(c(-(m:1), 1:m), 1:n)))
    # distances - only take those within a distance R.
    # and trim everything to this bound
    d <- sqrt((dx * ind[, 1])^2 + (dy * ind[, 2])^2)
    good <- (d > 0) & (d <= R)
    ind <- ind[good, ]
    d <- d[good]
    ind <- ind[order(d), ]
    d <- sort(d)
    #
    # arrays to hold statistics
    nbin <- nrow(ind)
    holdVG <- rep(NA, nbin)
    holdRVG <- rep(NA, nbin)
    holdN <- rep(NA, nbin)
    # loop over each separation
    for (k in 1:nbin) {
        # indices for original and shifted image that are within array bounds
        MM <- SI(M, ind[k, 1])
        NN <- SI(N, ind[k, 2])
        # number of differences and their values
        holdN[k] <- length(MM) * length(NN)
        BigDiff <- (dat[MM[, 1], NN[, 1]] - dat[MM[, 2], NN[, 
            2]])
        # standard and the  Cressie robust version.
        holdVG[k] <- mean(0.5 * (BigDiff)^2)
        holdRVG[k] <- mean(abs(BigDiff)^0.5)
    }
    # finish robust estimate Cressie (1993) formula 2.4.12
    holdRVG <- 0.5 * (holdRVG^4)/(0.457 + 0.494 * holdN)
    # collapsed variogram to common distances this what one would look
    # at under the stationary case.
    top <- tapply(holdVG * holdN, d, FUN = "sum")
    bottom <- tapply(holdN, d, FUN = "sum")
    dcollapsed <- as.numeric(names(bottom))
    vgram <- top/bottom
    #  wipe out pesky row names
    dimnames(vgram) <- NULL
    list(vgram = vgram, d = dcollapsed, ind = ind, d.full = d, 
        vgram.full = holdVG, robust.vgram = holdRVG, N = holdN, 
        dx = dx, dy = dy)
}