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Re: AI-GEOSTATS: Log transformation and zeros

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  • Ruben Roa
    ... on the outside limits of the distribution, so it can be possible to model the spatial behaviour in two steps. ... area out. ... if you can give me some
    Message 1 of 8 , Oct 3, 2002
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      >Hi
      >
      >I was investigating my data and it is possible to identifie areas of zeros
      on the outside limits of the distribution, so it can be possible to model
      the spatial behaviour in two steps.
      >
      >My guess is that I can simple reduce the kriging area to leave the zero
      area out.
      >
      >My doubt is how to model boundaries. I'm sure this is a common problem, so
      if you can give me some references I'll look forward to find them.
      >
      >Thanks and regards
      >
      >EJ

      Intrinsic geostatistics, the theory based on random functions, does not
      allow for 'boundary effects'. There should be no interaction between the
      random variable and its field (in practice, no decrease of density near the
      borders). On the other hand, transitive geostatistics, the theory based on
      purposive randomization, does allow for border effects and estimation of
      boundaries, which may fall anywhere between zero and non-zero observations.
      The difference between intrinsic and transitive geostatistics is as basic
      as the difference between model-unbiased and design-unbiased statistical
      inference.
      See
      Petitgas. 1993. Geostatistics for fish stock assessmens: a review and an
      acoustic application. ICES J Mar Sci 50:285-298.
      Petitgas and Lafont. 1997. EVA2: estimation variance. Version 2. A
      geostatistical software on Windows 95 for the precision of fish stock
      assessment surveys. ICES CM 1997/Y:22.

      Rubén
      http://webmail.udec.cl

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    • Brian R Gray
      while possibly outside the domain of your original question, I suspect that you may be able to treat your yield data as integers by treating the denominator as
      Message 2 of 8 , Oct 3, 2002
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        while possibly outside the domain of your original question, I suspect that
        you may be able to treat your yield data as integers by treating the
        denominator as an offset variable (as a technicality, I'd argue that, even
        after dividing by a constant, your data remain discrete--just not
        integers). this common technique would appear to take you back into the
        discrete world that Nicholas touched on. brian

        ****************************************************************
        Brian Gray
        USGS Upper Midwest Environmental Sciences Center
        575 Lester Avenue, Onalaska, WI 54650
        ph 608-783-7550 ext 19, FAX 608-783-8058
        brgray@...
        *****************************************************************



        Ernesto Jardim
        <ernesto@ipimar. To: Nicholas Lewin-Koh <nikko@...>
        pt> cc: Mailing List AI-Geostats <ai-geostats@...>
        Sent by: Subject: Re: AI-GEOSTATS: Log transformation and zeros
        ai-geostats-list
        @...


        10/02/2002 09:47
        AM
        Please respond
        to Ernesto
        Jardim





        Hi

        The data are not discrete. We collect number per hour, so it's a yield !

        Thanks

        EJ

        On Wed, 2002-10-02 at 15:27, Nicholas Lewin-Koh wrote:
        > Hi,
        > If the data are counts, ie integer number of fish and not tons, you
        > might want to try a discrete model such as a negative binomial or
        > Poisson. I have listed some references below, the top two have a more
        > Bayesian flavor.
        >
        > Nicholas
        >
        >
        > Alexander, N., Moyeed, R., Stander, J. (2000). Spatial modelling of
        > individual-level parasite counts using the negative binomial
        > distribution, Biostatistics, 2000, 1, 453-463.
        >
        > Diggle, P. J., Moyeed, R. A., Tawn, J. A. (1998). Model-based
        > geostatistics (with discussion), J. R. Statist. Soc. C, 47, 299-350.
        >
        > Gotway, C.A., Stroup, W.W. (1997) A Generalized Linear Model Approach
        > to Spatial Data Analysis and Prediction. Journal of Agricultural, Bio-
        > logical and Environmental Statistics 2(2), pp. 157­178.
        >
        >
        > On Wed, 2002-10-02 at 19:24, Ernesto Jardim wrote:
        > > Hi
        > >
        > > I'm analysing fisheries data (number of fish caught per hour) and I
        have
        > > some 0 values. When I log-trans I have to translate the values by
        hading
        > > some value.
        > >
        > > My question is which value is the best ? is there any works about this
        ?
        > >
        > > I usually had 1 so that I get values between 0 and infinite (no
        negative
        > > value) but I have doubts about it.
        > >
        > > Regards
        > >
        > > EJ
        > >
        > >
        > >
        > >
        > > --
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        > >
        > >
        > >
        >



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