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.elli...@lgcgroup.com > To: dimitriskarakost...@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}} ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
