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Re: [ai-geostats] confidence for predictions

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  • MARTINEZ VARGAS Adrian
    you do not give many details but if you have a multivariate system you can do: a) multivariate estimation/simulations with continuous variables. b)
    Message 1 of 2 , May 23, 2005
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      you do not give many details but if you have a multivariate system you
      can do:

      a) multivariate estimation/simulations with continuous variables.
      b) multivariate indicator estimation/simulations (that can include
      continuous variables to)

      Then compare the results by cross validation. Notice that if your binary
      logistic model have a nice classification table (no large error) and
      your multivariate estimation of continuous variables is accurate the: is
      possible that the option a) give good results because with indicators
      usually you loss a lot of resolution in your data and is too sensible to
      data density and distribution.

      cross validation error is a robust measure of your estimation...

      regard

      Adrian Martinez
      CFSG student
      Centre de Geostatistique
      Ecole des Mines de Paris


      Tib escribió:

      >Hi folks,
      >
      >I need suggestions on this simple question. I have built a logistic
      >regression model for predicting presence/absence for some kind of
      >species in a large area. The model does not incorporate any spatial
      >effect or longtitude or latitude. From this model I can get confidence
      >intervals for pridictions at all pixels in that area. My concern is,
      >can we measure the change of confidence against the distance to
      >observed points? Generally, the closer new observations to existed
      >ones (which are used to build the model), the higher confidence we
      >have for the pridictions because of intrinsic spatial autocorrelation.
      >Does variogram do this kind of job? Or any other references? Thank you
      >very much.
      >
      >
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