semivariogram calculation - discrepancy between gls and gstat
Guido, I suspect that nlme works with models for spatial correlation and not for spatial covariance, meaning that the variance (variog) gets standardized - see ?corClasses in nlme. Do the variog values approach 1 for longer distances?
On 07/05/2010 11:02 PM, Guido Lorenz wrote:
Dear R-sig-Geo members, a semivariogram for spatial soil data, calculated by the gls (nlme library) function gives the following estimates:
lmm.dap2b <- gls(Dap.sa ~ 1 + ID.sitio, Pd2006mm, correlation=corGaus(form=~(easting.m+northing.m)|ID.sitio, nugget=TRUE, metric="euclidean"), na.action=na.omit, method="REML") Variogram(lmm.dap2b)
variog dist n.pairs 1 0.3059585 2.828427 122 2 0.4040062 4.269282 125 3 0.5744688 18.110770 123 4 0.5266091 20.000000 125 .... whereas the variogram function of the gstat library gives, for similar distances (although different number of sample pairs), very different gamma values:
> variogram(Dap.sa ~ 1, locations = ~ easting.m + northing.m, data=Pd2006mm, cutoff=80)
np dist gamma dir.hor dir.ver id 1 219 3.225693 0.005578128 0 0 var1 2 20 6.478671 0.004254656 0 0 var1 3 3 13.513045 0.009896324 0 0 var1 4 307 19.201322 0.009708390 0 0 var1 ..... Can anyone explain what is happening? Thanks for any advice, Guido Lorenz Can anyone explain
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