Hello, I''m using the gstat package within R for an automated procedure that uses ordinary kriging. I can see that there is a logical ("singular") atrtibute of some adjusted model semivariograms: .- attr(*, "singular")= logi TRUE I cannot find documentation about the exact meaning and the implications of this attribute, and I dont know anything about the inner calculations of model semivariograms. I guess that the inverse of some matrix need to be calculated , and this matrix is singular, but I also see that the model semivariogram is calculated anyway. Could you briefly tell me something about the significance of this attribute and if I should not use these model semivariograms when the "singular" attibute is true? Thank you very much and best regards, Javier -- Javier Garc?a-Pintado Institute of Earth Sciences Jaume Almera (CSIC) Lluis Sole Sabaris s/n, 08028 Barcelona Phone: +34 934095410 Fax: +34 934110012 e-mail:jgarcia at ija.csic.es

Javier, consider two examples. First: > library(gstat) Loading required package: sp > data(meuse) > coordinates(meuse)=~x+y > variogram(log(zinc)~1,meuse,width=100,cutoff=200) np dist gamma dir.hor dir.ver id 1 52 77.01898 0.1299659 0 0 var1 2 263 156.23373 0.2091154 0 0 var1 > v = variogram(log(zinc)~1,meuse,width=100,cutoff=200) > vm = fit.variogram(v, vgm(1, "Exp", 100, 1)) Warning: singular model in variogram fit > attr(vm, "singular") [1] TRUE Here I try to fit a three-parameter model to two data (semivariance) points. Can''t be done, infinite number of solutions, indicated by the singularity flag. Second example: bad initial value for range: > v = variogram(log(zinc)~1,meuse,width=100,cutoff=1000) > vm = fit.variogram(v, vgm(1, "Sph", 10, 1)) Warning: singular model in variogram fit > attr(vm, "singular") [1] TRUE Starting with a range of 10, any combination of nugget and partial sill that fit the total sill improve the fit equally, indicated by the singularity. A larger value of the range (try 800) will lead to a good, non-singular fit. fit.variogram does usually a non-linear regression, so any problem in that area is potentially present. You may want to consider fixing certain parameters to avoid certain problems; look at the fit.sills and fit.ranges arguments of fit.variogram. In some cases, a singular model does fit the sample variogram nicely, e.g. where you use spherical or exponential models to effectively fit a linear semivariogram model: two parameters can be identified (nugget, slope) but three are fitted. The problem is to tell such a case from the two above, without looking at plots (i.e., automatically). -- Edzer javier garcia-pintado wrote:> Hello, > I''m using the gstat package within R for an automated procedure that > uses ordinary kriging. > I can see that there is a logical ("singular") atrtibute of some > adjusted model semivariograms: > > .- attr(*, "singular")= logi TRUE > > I cannot find documentation about the exact meaning and the implications > of this attribute, and I dont know anything about the inner calculations > of model semivariograms. > > I guess that the inverse of some matrix need to be calculated , and > this matrix is singular, but I also see that the model semivariogram is > calculated anyway. > > Could you briefly tell me something about the significance of this > attribute and if I should not use these model semivariograms when the > "singular" attibute is true? > > Thank you very much and best regards, > > Javier > > >