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#
# fields is a package for analysis of spatial data written for
# the R software environment.
# Copyright (C) 2024 Colorado School of Mines
# 1500 Illinois St., Golden, CO 80401
# Contact: Douglas Nychka, douglasnychka@gmail.com,
#
# 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
"predictSurface.mKrig" <- function(object, gridList=NULL, grid.list=NULL,
ynew = NULL,
extrap = FALSE, chull.mask = NA,
nx = 80, ny = 80,
xy = c(1,2), verbose = FALSE,
ZGrid=NULL, drop.Z= FALSE, just.fixed=FALSE,
fast=FALSE, NNSize=4, giveWarnings=FALSE,
derivative=0, ...) {
#
if( is.null(ZGrid) & !drop.Z & (!is.null(object$Z)) ) {
stop("Need to specify covariate (Z) values or set drop.Z==TRUE")
}
# grid.list is old syntax for fields gridList preferred
if (!is.null(grid.list)){ gridList<- grid.list}
# create a default grid if it is not passed
if (is.null(gridList)) {
# default is 80X80 grid on first variables in 1D
# first two in 2D or greater for > 2D
# rest are set to median value of the x's
gridList <- fields.x.to.grid(object$x, nx = nx, ny = ny,
xy = xy)
}
#print( gridList)
# do some checks on Zgrid and also reshape as a matrix
# rows index grid locations and columns are the covariates
# (as Z in predict).
# if ZGrid is NULL just returns NULL back ...
Z<- unrollZGrid( gridList, ZGrid)
xg <- make.surface.grid(gridList)
# NOTE: the predict function called will need to do some internal checks
# whether the evaluation of a large number of grid points (xg) makes sense.
if( verbose){
print( dim( xg))
print( nrow( xg))
print( drop.Z)
cat("dim of Z", fill=TRUE)
print( dim( Z))
}
if( nrow(xg) > 5e5){
warning("number of grid points is large for exact prediction
consider approximate prediction using fast==TRUE")
}
out<- rep( NA, nrow(xg))
# if extrapolate is FALSE only predict for locations inside convex hull
# (or the range in 1D)
indexGood <- rep(TRUE, nrow(xg))
if (!extrap) {
if (ncol(xg) > 1)
{
if (is.na(chull.mask)) {
chull.mask <- unique.matrix(object$x[, xy])
}
indexGood <-
in.poly(xg[, xy], xp = chull.mask, convex.hull = TRUE)
}
else{
# 1D case
indexGood <- xg[, 1] >= min(object$x[, 1]) &
xg[, 1] <= max(object$x[, 1])
}
}
if(!fast){
# here is the heavy lifting
out[indexGood] <- predict.mKrig(object, xnew=xg[indexGood,], ynew=ynew,
Z=Z[indexGood,], drop.Z= drop.Z,
collapseFixedEffect = object$collapseFixedEffect,
just.fixed=just.fixed, ...)
}
else{
# fast approximate method
# will always predict to full grid.
out<- mKrigFastPredict( object,
gridList= gridList,
ynew = ynew,
derivative = derivative,
Z = Z, drop.Z = drop.Z,
NNSize=NNSize,
giveWarnings=giveWarnings,
...
)
# wipe out predictions outside convex hull of observations.
if(!extrap){
out[ !indexGood]<- NA
}
}
# reshape as list with x, y and z components
out <- as.surface( xg, out )
#
#
return(out)
}
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