

> 
> 
> Thanks for the answer. I have already read the gstat manual and I had constructed the empirical and theoretical variogram like this:

g <- gstat(id="tec", formula=TEC ~ 1, data=data)v <- variogram(g)mod<-vgm(sill=var(data$TEC),model="Sph",range=200,nugget=10)v.fit <- fit.variogram(v, model=mod,fit.method=1)Theor_variogram=plot(variogram(TEC~1,data),v.fit,main="WLS Model")plot(Theor_variogram)>
> But still, when I use predict:   p <- predict.gstat(g, model=v.fit, newdata=predGrid)
> ..instead of ordinary kriging I get inverse distance weighted.Please, if anyone knows where I make the mistake or what I miss, please let me know!Thanks
>  
> 
> 
> 
> > From: S.Ellison@LGCGroup.com
> > To: dimitriskarakostis3@hotmail.com; r-help@r-project.org
> > Date: Mon, 17 Dec 2012 17:22:12 +0000
> > Subject: RE: [R] How to make Ordinary Kriging using gstat predict?
> > 
> > 
> > > -----Original Message-----
> > > My problem is that instead of Ordinary kriging, when I run 
> > > the algorithm I get: Inverse distance weighted interpolation. 
> > > Why is that? What am I missing or doing wrong? 
> > The gstat manual at http://www.gstat.org/gstat.pdf says on p16 that  "When no variograms are specified, inverse distance weighted interpolation
> > is the default action (Fig. 2.1, example [6.3]).
> > When variograms are specified the default prediction method is ordinary
> > kriging Journel and Huijbregts (1978); Cressie (1993) (example [6.4] and
> > example [6.8])."
> > 
> > It looks like reading that manual may be useful ...
> > 
> > S Ellison
> > 
> > *******************************************************************
> > This email and any attachments are confidential. Any u...{{dropped:11}}
> 
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