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Fig. 5.15: Anisotropy (left) and variogram model fitted using the Maximum Likelihood (ML) method (right).

T.Hengl - Tue, 12/08/2009 - 18:01
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Fig. 5.15: Anisotropy (left) and variogram model fitted using the Maximum Likelihood (ML) method (right).
data(meuse)
coordinates <- ~x+y
zinc.geo <- as.geodata(meuse["zinc"]) 
str(zinc.geo)
# plot(zinc.geo)
# Variogram modelling (target variable):
par(mfrow=c(1,2))
# anisotropy ("lambda=0" indicates log-transformation):
plot(variog4(zinc.geo, lambda=0, max.dist=1500, messages=FALSE), lwd=2)
# fit variogram using likfit:
zinc.svar2 <- variog(zinc.geo, lambda=0, max.dist=1500, messages=FALSE)
zinc.vgm2 <- likfit(zinc.geo, lambda=0, messages=FALSE, ini=c(var(log1p(zinc.geo$data)),500), cov.model="exponential")
zinc.vgm2
# this carries much more information!
env.model <- variog.model.env(zinc.geo, obj.var=zinc.svar2, model=zinc.vgm2)
plot(zinc.svar2, envelope=env.model); lines(zinc.vgm2, lwd=2);
legend("topleft", legend=c("Fitted variogram (ML)"), lty=c(1), lwd=c(2), cex=0.7)
dev.off()