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Re: AI-GEOSTATS: Log transformation and zeros

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  • Ernesto Jardim
    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
    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

      On Wed, 2002-10-02 at 19:33, Donald E. Myers wrote:
      > Adding a constant to all values will shift the distribution but will not
      > change its shape. If the fraction of zeros is large then you will likely
      > not have a lognormal distribution and hence taking logs may not solve
      > the problem. If you intend using kriging (after applying a log
      > transform) then you will have to worry about the bias correction when
      > you re-transform, to do that the theoretical solution requires
      > multivariate lognormality (univariate is not sufficient).
      >
      > You might want to look at the spatial pattern of the zeros, i.e., is it
      > plausible to separate the data set spatially and have most of the zeros
      > in only one region?
      >
      > Donald E. Myers
      > http://www.u.arizona.edu/~donaldm
      >
      > 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
      > >
      > >
      > >
      > >
      > >--
      > >* To post a message to the list, send it to ai-geostats@...
      > >* As a general service to the users, please remember to post a summary of any useful responses to your questions.
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      > >
      > >
      >



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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 2 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 3 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
          > >
          > >
          > >
          > >
          > > --
          > > * To post a message to the list, send it to ai-geostats@...
          > > * As a general service to the users, please remember to post a summary
          of any useful responses to your questions.
          > > * To unsubscribe, send an email to majordomo@... with no subject
          and "unsubscribe ai-geostats" followed by "end" on the next line in the
          message body. DO NOT SEND Subscribe/Unsubscribe requests to the list
          > > * Support to the list is provided at http://www.ai-geostats.org
          > >
          > >
          > >
          >



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