Hi Dimitris,
The mistake is that predict.gstat doesnt have a "model" argument, as you
assume. But as the function also accepts arguments through ..., it does
not complain about the unused argument.
Try instead to put the model argument in the gstat-object as you can see
in the example in ?predict.gstat:
g <- gstat(id="tec", formula=TEC ~ 1, data=data, model = v.fit)
Cheers,
Jon
BTW, you will generally get quicker response to questions regarding any
kind of spatial data handling from the mailinglist r-sig-...@r-project.org.
On 17-Dec-12 18:58, DIMITRIS KARAKOSTIS wrote:
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
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