https://github.com/cran/gstat
Tip revision: 9ff36b78a3874a6c44b1d1138c294f4f814ad466 authored by Edzer Pebesma on 31 August 2012, 08:55:56 UTC
version 1.0-14
version 1.0-14
Tip revision: 9ff36b7
krige0.R
extractFormula = function(formula, data, newdata) {
# extract y and X from data:
m = model.frame(terms(formula), as(data, "data.frame"))
y = model.extract(m, "response")
if (length(y) == 0)
stop("no response variable present in formula")
Terms = attr(m, "terms")
X = model.matrix(Terms, m)
# extract x0 from newdata:
terms.f = delete.response(terms(formula))
mf.f = model.frame(terms.f, newdata) #, na.action = na.action)
x0 = model.matrix(terms.f, mf.f)
list(y = y, X = X, x0 = x0)
}
idw0 = function(formula, data, newdata, y) {
s = coordinates(data)
s0 = coordinates(newdata)
if (missing(y))
y = extractFormula(formula, data, newdata)$y
D = 1.0 / (spDists(s0, s) ** 2)
sumD = apply(D, 1, sum)
D %*% y / sumD
}
CHsolve = function(A, b) {
# solves A x = b for x if A is PD symmetric
#A = chol(A, LINPACK=TRUE)
A = chol(A)
backsolve(A, forwardsolve(A, b, upper.tri = TRUE, transpose = TRUE))
}
krige0 <- function(formula, data, newdata, model, beta, y, ...,
computeVar = FALSE, fullCovariance = FALSE) {
stopifnot(identical(proj4string(data), proj4string(newdata)))
lst = extractFormula(formula, data, newdata)
X = lst$X
x0 = lst$x0
if (missing(y))
y = lst$y
ll = (!is.na(is.projected(data)) && !is.projected(data))
s = coordinates(data)
s0 = coordinates(newdata)
if (is(model, "variogramModel")) {
require(gstat)
V = variogramLine(model, dist_vector = spDists(s, s, ll),
covariance=TRUE)
v0 = variogramLine(model, dist_vector = spDists(s, s0, ll),
covariance=TRUE)
c0 = variogramLine(model, dist_vector = c(0), covariance=TRUE)$gamma
} else {
V = model(data)
v0 = model(data, newdata)
if (computeVar) {
if (is(newdata, "SpatialLines") ||
is(newdata, "SpatialPolygons"))
stop("varying target support has not been implemented")
c0 = as.numeric(model(newdata[1, drop=FALSE],
newdata[1, drop=FALSE]))
# ?check this: provide TWO arguments, so model(x,y) can target
# eventually Y, instead of measurements Z=Y+e
# with e measurement error term e
}
}
if (!missing(beta)) { # sk:
skwts = CHsolve(V, v0)
if (computeVar)
var = c0 - t(v0) %*% skwts
} else { # ok/uk -- need to estimate beta:
skwts = CHsolve(V, cbind(v0, X))
ViX = skwts[,-(1:nrow(s0))]
skwts = skwts[,1:nrow(s0)]
beta = solve(t(X) %*% ViX, t(ViX) %*% y)
if (computeVar) {
# here done the HARD, i.e. m x m way; first compute
# (x0-X'C-1 c0)'(X'C-1X)-1 (x0-X'C-1 c0)
# -- precompute term 1+3:
Q = t(x0) - t(ViX) %*% v0
var = c0 - t(v0) %*% skwts + t(Q) %*% CHsolve(t(X) %*% ViX, Q)
}
}
pred = x0 %*% beta + t(skwts) %*% (y - X %*% beta)
if (computeVar) {
if (!fullCovariance)
var = diag(var)
list(pred = pred, var = var)
} else
pred
}
krigeST <- function(formula, data, newdata, modelList, y, ...,
computeVar = FALSE, fullCovariance = FALSE) {
if (is(data, "ST") && is(newdata, "ST")) {
stopifnot(identical(proj4string(data@sp), proj4string(newdata@sp)))
stopifnot(is(data, "STFDF"))
}
lst = extractFormula(formula, data, newdata)
X = lst$X
x0 = lst$x0
if (missing(y))
y = lst$y
V = covfn.ST(data, model = modelList)
stopifnot(!is.null(V$T) && !is.null(V$S))
v0 = covfn.ST(data, newdata, model = modelList)
stopifnot(!is.null(v0$T) && !is.null(v0$S))
d0 = data[1, 1, drop=FALSE]
c0 = as.numeric(covfn.ST(d0, d0, separate = FALSE, model = modelList))
skwts = STsolve(V, v0, X)
#ViX = skwts[,-(1:ncol(v0))]
#skwts = skwts[,1:ncol(v0)]
npts = prod(dim(newdata)[1:2])
ViX = skwts[,-(1:npts)]
skwts = skwts[,1:npts]
beta = solve(t(X) %*% ViX, t(ViX) %*% y)
pred = x0 %*% beta + t(skwts) %*% (y - X %*% beta)
if (computeVar) {
# get (x0-X'C-1 c0)'(X'C-1X)-1 (x0-X'C-1 c0) -- precompute term 1+3:
v0 = v0$Tm %x% v0$Sm
Q = t(x0) - t(ViX) %*% v0
var = c0 - t(v0) %*% skwts + t(Q) %*% CHsolve(t(X) %*% ViX, Q)
if (!fullCovariance)
var = diag(var)
list(pred = pred, var = var)
} else
pred
}
STsolve = function(A, b, X) {
# V = A$T %x% A$S -- a separable covariance; solve A x = b for x
# kronecker: T %x% S vec(L) = vec(c0) <--> S L T = c0
# solve for L: use Y = L T
# S Y = c0 -> Y = solve(S, c0)
# L T = Y -> Tt Lt = Yt -> Lt = solve(Tt, Yt)
#Tm = chol(A$Tm, LINPACK=TRUE)
Tm = chol(A$Tm)
#Sm = chol(A$Sm, LINPACK=TRUE)
Sm = chol(A$Sm)
STbacksolve = function(Tm, Cm, Sm) {
MyChSolve = function(A, b)
backsolve(A, forwardsolve(A, b, upper.tri = TRUE, transpose = TRUE))
# Y = MyChSolve(Sm, Cm)
# L = MyChSolve(Tm, t(Y))
# as.vector(t(L))
as.vector(t(MyChSolve(Tm, t(MyChSolve(Sm, Cm)))))
}
# b comes separated:
ret1 = apply(b$T, 2, function(x1)
apply(b$S, 2, function(x2)
STbacksolve(Tm, matrix(x1 %x% x2, nrow(Sm), nrow(Tm)), Sm)))
d = dim(ret1)
dim(ret1) = c(d[1] / ncol(b$S), d[2] * ncol(b$S))
# X comes full:
ret2 = apply(X, 2, function(x)
STbacksolve(Tm, matrix(x, nrow(Sm), nrow(Tm)), Sm))
#print(dim(ret1))
#print(dim(ret2))
cbind(ret1, ret2)
}
covfn.ST = function(x, y = x, separate = TRUE, model) {
if (is(model$space, "variogramModel"))
Sm = variogramLine(model$space, covariance = TRUE, dist_vector =
spDists(coordinates(x@sp), coordinates(y@sp)))
else
Sm = model$space(x, y)
if (is(model$time, "variogramModel"))
Tm = variogramLine(model$time, covariance = TRUE, dist_vector =
abs(outer(as.numeric(index(x@time)), as.numeric(index(y@time)), "-")))
else
Tm = model$time(x, y)
if (separate)
list(Sm = Sm, Tm = Tm)
else
Tm %x% Sm # kronecker product
}