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Re: AI-GEOSTATS: Crossvalidation

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  • Soeren Nymand Lophaven
    Dear Victoria Firstly, from the output you have displayed it seems to me that you should take the natural logarithm of your observations before doing the
    Message 1 of 2 , Aug 7, 2002
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      Dear Victoria

      Firstly, from the output you have displayed it seems to me that you should
      take the natural logarithm of your observations before doing the spatial
      analysis.

      Secondly, if you want a measure of how well your model is predicting you
      could use sum((pred.-obs.)^2). That would in your case be the sum of 118
      squared differences. If you then try to predict your response using
      different types of models, you should chose the model with the lowest
      sum((pred.-obs.)^2).

      Best regards / Venlig hilsen

      Søren Lophaven
      ******************************************************************************
      Master of Science in Engineering | Ph.D. student
      Informatics and Mathematical Modelling | Building 321, Room 011
      Technical University of Denmark | 2800 kgs. Lyngby, Denmark
      E-mail: snl@... | http://www.imm.dtu.dk/~snl
      Telephone: +45 45253419 |
      ******************************************************************************

      On Wed, 7 Aug 2002, Victoria Íñigo Mendoza wrote:

      > Dear list members:
      > I'm doing my PhD about forestry soils and I use Idrisi 3.2 for my
      > geostatistical analysis with Gstat interface. I do Ordinary Kriging and
      > after fitting the model when I do the crossvalidation I have to
      > interpret this factors. Which of those have I pay attention in, to
      > decide the best model?:
      >
      > corr(Obs, Pred): 0.1709 [using ordinary kriging]
      >
      > observed predicted pred.-obs. pred.std. zscore
      > ======================================================================
      > minimum 26.6 225.9 -3043 352 -8.528
      > 1st q. 224.6 379.8 -119.9 367.9
      > -0.3343
      > median 360.4 499.3 114 373.8 0.3052
      >
      > 3rd q. 653.1 658.8 314.4 383.9
      > 0.8117
      > maximum 3685 1658 1173 418.5 3.166
      >
      > n 118 118 118 118
      > 118
      > mean 552.3 558.9 6.545 376.1 0.01326
      > std.dev. 616.1 236.5 621 13.77 1.679
      >
      >
      > Thanks and best regards
      > Victoria Iñigo Mendoza
      > University of La Rioja
      > Spain
      >
      >
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