https://github.com/cran/fields
Tip revision: 328f151e07864e976578f9134025cbfdb72eb2f3 authored by Doug Nychka on 19 July 2013, 23:45:29 UTC
version 6.8
version 6.8
Tip revision: 328f151
as.image.R
# 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
"as.image" <- function(Z, ind = NULL, grid = NULL,
x = NULL, nrow = 64, ncol = 64, weights = NULL, na.rm = FALSE,
nx = NULL, ny = NULL, boundary.grid = FALSE) {
# NOTE that throughout ind is a two column integer matrix of
# discretized locations in the image matrix.
# from different reductions due to NAs the final
# versions of Z, x, and weights may be a subset of the
# passed versions.
# Thanks to J. Rougier for fixing bugs in this function.
# set some default values for arguments
#
# coerce Z to a vector
Z <- c(Z)
# use indicators and weights if passed
if (!is.null(ind))
x <- ind
if (is.null(weights)) {
weights <- rep(1, length(Z))
}
#
# use values of nx ny if passed; these are just different names for
# nrow, ncol.
if (!is.null(nx))
nrow <- nx
if (!is.null(ny))
ncol <- ny
##### end of setting defaults
# check for missing values in Z and na.rm==FALSE
if (any(is.na(Z)) & !na.rm) {
stop("missing values in Z, set na.rm=TRUE")
}
#
# if there are missing overwrite Z, x, ind and weights.
#
if (any(is.na(Z))) {
Z.good <- !is.na(Z)
Z <- Z[Z.good]
x <- x[Z.good, ]
ind <- ind[Z.good, ]
weights <- weights[Z.good]
}
#
# check for x or weights having missing values
# we do not like these ...
if (any(is.na(weights)) | any(is.na(c(x)))) {
stop("missing values in weights or x")
}
# discretize locations to grid boxes
# this function will also create a default grid based on range of
# locations if is NULL
#
temp <- discretize.image(x, m = nrow, n = ncol, grid = grid,
boundary.grid = boundary.grid)
nrow <- temp$m
ncol <- temp$n
ind <- temp$index
grid <- temp$grid
# ind is a two column matrix with the index of the x and y grid points.
# NOTE points outside of grid are NAs.
#
#
# if any of the ind's rows are NA's it means that the x's were
# outside the range of the grid.
# pare down arguments and eliminate these points.
#
good.ind <- !(is.na(ind[, 1]) | is.na(ind[, 2]))
if (any(!good.ind)) {
warning("Some locations are outside the grid limits")
ind <- ind[good.ind, ]
Z <- Z[good.ind]
weights <- weights[good.ind]
}
#
# find unique set of boxes for the locations
#
#
rep.info <- cat.matrix(ind)
uniquerows <- !duplicated(rep.info)
#
# compute weighted means where there are replicates
#
# NOTE that in the assignments below we use the
# fact that a 2 column matrix (i.e. ind) is interpreted as a multiple index.
#
if (sum(uniquerows) < length(Z)) {
# this means that some Z's are in the same box
ind <- ind[uniquerows, ]
temp <- fast.1way(rep.info, Z, w = weights)
# over write Z with weighted means
Z <- temp$means
temp3 <- matrix(NA, nrow = nrow, ncol = ncol)
temp3[ind] <- temp$w.means
}
else {
temp3 <- matrix(NA, nrow = nrow, ncol = ncol)
temp3[ind] <- 1
}
temp <- matrix(NA, nrow = nrow, ncol = ncol)
temp[ind] <- Z
call <- match.call()
list(x = grid$x, y = grid$y, z = temp, call = call, ind = ind,
weights = temp3, xd = cbind(grid$x[ind[, 1]], grid$y[ind[,
2]]), call = match.call())
}