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Re: AI-GEOSTATS: kriging estimates in S+

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  • Edzer J. Pebesma
    The spatial library in S-Plus (or R) does not have a function called kige , or krige . Could you please be more precise what you did? -- Edzer ... -- * To
    Message 1 of 7 , May 9 2:14 AM
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      The spatial library in S-Plus (or R) does not have a function
      called "kige", or "krige". Could you please be more precise
      what you did?
      --
      Edzer

      vanessa stelzenmüller wrote:
      >
      > Dear list members,
      >
      > I have a question concerning the kriging results
      > derived from "kige" of the spatial modul for S+.
      >
      > The values for the variable of interest estimated with
      > Ordinary Kriging and Universal Kriging at the sampled
      > locations differ from the one observed!
      >
      > Does anyone has a suggestion why?
      >

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    • vanessa stelzenmüller
      Edzer, I used krige under S+ SpatialStats with the specified model (gaussian with nugget) to perform ordinary and universal kriging for two dimensional
      Message 2 of 7 , May 9 2:40 AM
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        Edzer,

        I used "krige" under S+ SpatialStats with the
        specified model (gaussian with nugget) to perform
        ordinary and universal kriging for two dimensional
        spatial data. Afterwards I used "predict" as well as
        "predict.krige" to compute point kriging predictions
        and standard errors at specified locations ( I defined
        a grid). The data are sampled at a regular grid.
        Finally I recognized that the predicted and observed
        values for sampled locations are no identical.

        Best wishes
        Vanessa

        =====
        Vanessa Stelzenmüller,PhD-Student
        ICBM, Dep. Aquatic Ecology
        C.v.O University of Oldenburg
        P.O. Box 2503
        26111 Oldenburg
        <º)))>< <º)))><
        Tel:+49 441-798 3306, Fax:+49 441-798 3701

        __________________________________________________________________

        Gesendet von Yahoo! Mail - http://mail.yahoo.de
        Logos und Klingeltöne fürs Handy bei http://sms.yahoo.de

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      • Edzer J. Pebesma
        Vanessa, you are right. I was confused, as MASS has a free library called spatial , but you meant the commercial add-on module S+SpatialStats , which is also
        Message 3 of 7 , May 9 4:05 AM
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          Vanessa,

          you are right. I was confused, as MASS has a free library
          called "spatial", but you meant the commercial add-on module
          "S+SpatialStats", which is also called "module spatial" by
          the vendor of S-Plus.

          It seems that S+SpatialStats, when predicting at point locations,
          does as if the locations are shifted a very small amount; I guess
          this is saying the same thing that Isobel meantioned earlier (see
          my sample below). When predicting without a nugget effect, the
          differences are also too large, IMO (see x2 example); I do not
          have an explanation for that.

          I also tried the examples with the (my) gstat S library, which can
          be downloaded from http://www.gstat.org/ , where both examples do
          what you'd expect; again see below.

          Thanks for sharing this with us.
          --
          Edzer

          Here's the output of the S-Plus session:

          S-PLUS : Copyright (c) 1988, 2002 Insightful Corp.
          S : Copyright Lucent Technologies, Inc.
          Version 6.1.2 Release 2 for Linux 2.2.12 : 2002
          Working data will be in .Data
          > module(spatial)
          > x<-as.numeric(1:10)
          > y<-as.numeric(1:10)
          > z<-as.numeric(1:10)
          > d<-data.frame(x=x,y=y,z=z)
          > d
          x y z
          1 1 1 1
          2 2 2 2
          3 3 3 3
          4 4 4 4
          5 5 5 5
          6 6 6 6
          7 7 7 7
          8 8 8 8
          9 9 9 9
          10 10 10 10
          > krige(z~loc(x,y),data=d,covfun=gauss.cov,range=3,sill=1,nugget=1)
          Call:
          krige(formula = z ~ loc(x, y), data = d, covfun = gauss.cov, range = 3, sill =
          1, nugget = 1)

          Coefficients:
          constant
          5.5

          Number of observations: 10
          > x<-krige(z~loc(x,y),data=d,covfun=gauss.cov,range=3,sill=1,nugget=1)
          > predict(x, d)
          x y fit se.fit
          1 1 1 2.827990 1.206120
          2 2 2 2.778034 1.155678
          3 3 3 3.432520 1.152567
          4 4 4 4.299386 1.153139
          5 5 5 5.113279 1.153538
          6 6 6 5.886721 1.153538
          7 7 7 6.700614 1.153139
          8 8 8 7.567480 1.152567
          9 9 9 8.221966 1.155678
          10 10 10 8.172010 1.206120
          > d2<-data.frame(x=y+1e-7,y=y+1e-7)
          > d2
          x y
          1 1 1
          2 2 2
          3 3 3
          4 4 4
          5 5 5
          6 6 6
          7 7 7
          8 8 8
          9 9 9
          10 10 10
          > predict(x, d2)
          x y fit se.fit
          1 1 1 2.827990 1.206120
          2 2 2 2.778034 1.155678
          3 3 3 3.432520 1.152567
          4 4 4 4.299386 1.153139
          5 5 5 5.113279 1.153538
          6 6 6 5.886721 1.153538
          7 7 7 6.700614 1.153139
          8 8 8 7.567480 1.152567
          9 9 9 8.221966 1.155678
          10 10 10 8.172010 1.206120
          > x2<-krige(z~loc(x,y),data=d,covfun=gauss.cov,range=3,sill=2,nugget=0)
          > predict(x2, d)
          x y fit se.fit
          1 1 1 1.000004 0.001272791
          2 2 2 1.999994 0.001272785
          3 3 3 3.000009 0.001272774
          4 4 4 3.999990 0.001272763
          5 5 5 5.000010 0.001272757
          6 6 6 5.999990 0.001272757
          7 7 7 7.000010 0.001272763
          8 8 8 7.999991 0.001272774
          9 9 9 9.000006 0.001272785
          10 10 10 9.999996 0.001272791
          >
          # Now using the gstat library:
          > library(gstat)
          > krige(z~1,~x+y,d,d,vgm(1,"Gau",3,1))
          [using ordinary kriging]
          x y var1.pred var1.var
          1 1 1 1 0.000000e+00
          2 2 2 2 0.000000e+00
          3 3 3 3 5.185705e-33
          4 4 4 4 2.074282e-32
          5 5 5 5 5.185705e-33
          6 6 6 6 2.074282e-32
          7 7 7 7 0.000000e+00
          8 8 8 8 5.185705e-33
          9 9 9 9 0.000000e+00
          10 10 10 10 0.000000e+00
          > krige(z~1,~x+y,d,d,vgm(2,"Gau",3,0))
          [using ordinary kriging]
          x y var1.pred var1.var
          1 1 1 1 7.106437e-33
          2 2 2 2 0.000000e+00
          3 3 3 3 4.440892e-16
          4 4 4 4 2.220446e-16
          5 5 5 5 2.220446e-16
          6 6 6 6 4.440892e-16
          7 7 7 7 2.220446e-16
          8 8 8 8 2.842575e-32
          9 9 9 9 2.842575e-32
          10 10 10 10 0.000000e+00
          >

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        • Mary C. Christman
          Hello Vanessa, I am guessing that the reason the ordinary krige (OK) and the universal krige (UK) give different results for the observed locations is due to
          Message 4 of 7 , May 9 12:04 PM
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            Hello Vanessa,

            I am guessing that the reason the ordinary krige (OK) and
            the universal krige (UK) give different results for the
            observed locations is due to the trend fit in the
            UK. The results you are getting from the UK are probably
            the predicted values from the trend fit. Does the
            predict.krige function provide the two components (trend
            versus kriged residual) separately or does it give only the
            combined predicted value?

            Mary Christman


            On Fri, 9 May 2003 11:40:32 +0200 (CEST)
            =?iso-8859-1?q?vanessa=20stelzenm=FCller?=
            <vstelzenmueller@...> wrote:

            > Edzer,
            >
            > I used "krige" under S+ SpatialStats with the
            > specified model (gaussian with nugget) to perform
            > ordinary and universal kriging for two dimensional
            > spatial data. Afterwards I used "predict" as well as
            > "predict.krige" to compute point kriging predictions
            > and standard errors at specified locations ( I defined
            > a grid). The data are sampled at a regular grid.
            > Finally I recognized that the predicted and observed
            > values for sampled locations are no identical.
            >
            > Best wishes
            > Vanessa
            >
            > =====
            > Vanessa Stelzenm�ller,PhD-Student
            > ICBM, Dep. Aquatic Ecology
            > C.v.O University of Oldenburg
            > P.O. Box 2503
            > 26111 Oldenburg
            > <�)))>< <�)))><
            > Tel:+49 441-798 3306, Fax:+49 441-798 3701
            >
            > __________________________________________________________________
            >
            > Gesendet von Yahoo! Mail - http://mail.yahoo.de
            > Logos und Klingelt�ne f�rs Handy bei http://sms.yahoo.de
            >
            > --
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            ----------------------
            Mary C. Christman
            Department of Animal and Avian Sciences
            University of Maryland
            College Park, MD 20742
            fax: 301-405-7980
            office: 301-405-8867
            email: mc276@...


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