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Kriging
Kriging is related to trend surface analysis except (as Venebles and
Ripley show), an explicit "covariance structure" of the residuals of the
model is included in the model specification in the surf.gls() function,
where the form of the covariance structure is determined by inspecting
the spatial correlogram and variogram of the variable being contoured.
# Kriging
library(spatial)
oldpar <- par(mfrow = c(2, 2))
# variogram and covariogram ls
fit.ls1 <- surf.ls(2, ctrl.data)
sar <- 0.7
nl <- 25
correlogram(fit.ls1, nl)
xd <- seq(0, 8, 0.1)
lines(xd, expcov(xd, sar))
variogram(fit.ls1, nl)
# variogram and covariogram gls
d <- 0.7
fit.gls1 <- surf.gls(2, expcov, ctrl.data, d=0.7)
nl <- 25
correlogram(fit.gls1, nl)
xd <- seq(0, 8, 0.1)
lines(xd, expcov(xd, d))
variogram(fit.gls1, nl)
# model fitting and contouring
ctrl.data <- data.frame(x, y, z)
fit.kr2 <- surf.gls(4, expcov, d=50.0, ctrl.data, nx=100)
interp.kr2 <- trmat(fit.kr2, -124.5000, -116.8333, 42.0000, 46.1667, 30)
plot(latitude ~ longitude, main="Kriging", type="n")
contour(interp.kr2, add=T)
points(latitude ~ longitude)
par(oldpar)
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