1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
|
#
# 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.Krig" <- 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, just.fixed=FALSE, ...) {
if( is.null(ZGrid) & !drop.Z & (!is.null(object$Z)) ) {
stop("Need to specify covariate (Z) values or set drop.Z==TRUE")
}
# create a default grid if it is not passed
if (is.null(grid.list)) {
# in more than 2-D
# default is 80X80 grid on first two variables
# rest are set to median value of the x's
grid.list <- fields.x.to.grid(object$x, nx = nx, ny = ny,
xy = xy)
}
# do some checks on Zgrid and also reshape as a matrix
# rows index grid locations and columns are the covariates
# (as Z in predict).
# if ZGrid is NULL just returns that back
Z<- unrollZGrid( grid.list, ZGrid)
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.
if( verbose){
print( dim( xg))
print( nrow( xg))
print( drop.Z)
print( dim( Z))
}
# if extrapolate is FALSE set all values outside convex hull to NA
if (!extrap) {
if( is.null( object$x)){
stop("need and x matrix in object")
}
if (is.na(chull.mask)) {
chull.mask <- unique.matrix(object$x[, xy])
}
indexGood<- in.poly(xg[, xy], xp = chull.mask, convex.hull = TRUE)
if( verbose){
print( sum( indexGood) )
}
}
else{
indexGood<- rep( TRUE, nrow( xg))
}
out<- rep( NA, nrow(xg))
# here is the heavy lifting
out[indexGood] <- predict(object, x=xg[indexGood,], Z=Z[indexGood,], drop.Z= drop.Z,
just.fixed=just.fixed, ...)
# reshape as list with x, y and z components
out <- as.surface( xg, out )
#
#
return(out)
}
|
