R/predictSurface.LKrig.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

"predictSurface.LKrig" <- function(object, grid.list = NULL, 
	extrap = FALSE, chull.mask = NA, nx = 80, ny = 80, 
	xy = c(1, 2), verbose = FALSE, ZGrid = NULL, drop.Z = FALSE, 
	...) {
	# NOTE: 
	# without grid.list
# default is 80X80 grid on first two variables
# rest are set to median value of x.
if (is.null(ZGrid) & !drop.Z & (!is.null(object$Z))) {
		stop("need to specify covariate values or set drop.Z==TRUE")
	}
	# create a default grid if it is not passed    
	if (is.null(grid.list)) {
		grid.list <- fields.x.to.grid(object$x, nx = nx, 
			ny = ny, xy = xy)
	}
  
	# do some checks on Zgrid and also reshape as a matrix
	# rows index grid locations and columns the covariates (like Z in predict).
  Z <- LKrigUnrollZGrid(grid.list, ZGrid)
	# here is the heavy lifting
	xg <- make.surface.grid(grid.list)
	
	# NOTE: the predict function called will need to do some internal  the checks
	# whether the evaluation of a large number of grid points (xg)  makes sense.
	out <- predict.LKrig(object, xg, Znew = Z, drop.Z = drop.Z, 
		...)

	# reshape as list with x, y and z components    
	out <- as.surface(xg, out)
	#
	# if extrapolate is FALSE set all values outside convex hull to NA
if (!extrap) {
		if (is.null(object$x)) {
			stop("need an x matrix in object")
		}
		if (is.na(chull.mask)) {
			X <- unique.matrix(object$x[, xy])
			# x can just be the lower left and upper right corners
			# add all four
			if( nrow( X)==2){
			  xr<- range(X[,1])
			  yr<- range(X[,2])
			  chull.mask<- rbind( c(xr[1], yr[1]),
			                      c(xr[2], yr[1]),
			                      c(xr[2], yr[2]),
			                      c(xr[1], yr[2])
			                     )
			}
			else{
			  chull.mask<- X
			}
			
		}
		out$z[!in.poly(xg[, xy], xp = chull.mask,
		                      convex.hull = TRUE)] <- NA
	}
	#
	return(out)
}