Here is the chunk where I create variograms for multiple directions:
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#Additional to the explanation above on model.auto(), if you included many basic structures into the auto-fitting algorithm of RGeostats, model.auto() will try to fit every included structure and #pick out the best fit to plot with the experimental variogram.
data.model.omni = model.auto(vario.omni,
struct = c("Spherical", "Gaussian", "Exponential"),
title = paste(property, "Model Omnidirectional"), pos.legend=1,
xlab = "Log distance",
ylab = expression(paste("Variance (", gamma, "(h))", sep="")))
Here is the chunk where I specify an omnidirectional variogram to use in Kriging:
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dbgrid.omni <- dbgrid
dbgrid.omni = kriging(db, dbgrid, data.model.omni, neigh.unique, radix="NewOmni")
plot(dbgrid.omni, pos.legend=1, cex=0.7,
title = paste(property, "Kriging with omni-directional Model"),xlab = "X (UTM)", ylab = "Y (UTM)")
Here is a chunk where I specify a directional variogram:
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#Using vario.calc() again, this time to calculate specific directions such as 0, 90, degrees
data.2dir.vario = vario.calc(db, nlag=10, dir=c(0,90))
Here is where I specify to plot the variogram:
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plot(data.2dir.vario,type = "p", pch = 19, npairpt=T, npairdw=F, pos.legend = 1, cex = 0.7,
title = paste(property, "Experimental 3 Directional Variogram"),
xlab = "Log distance", ylab = expression(paste("Variance (", gamma, "(h))", sep="")))
Here is where I specify the directional variogram to use in kriging...
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dbgrid.2dir.vario <- dbgrid
dbgrid.2dir.vario = kriging(db, dbgrid.2dir, data.model.2dir, neigh.unique, radix="New2Dir")
plot(dbgrid.2dir, pos.legend=1, cex=0.7,
title = paste(property, "Kriging with directional-Model"),xlab = "X (UTM)", ylab = "Y (UTM)")
It appears the direction '0' is East and '90' is north (mathematical directions)
I can't past the images here, but it does seem that the omni directional kriging results and the directional results are virtually the same...
Any suggestions would be appreciated.
Jeffrey Yarus