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AI-GEOSTATS: backtransformation

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  • Marta Rufino
    Dear list members, I would like to know if anyone has information or bibliography on backtransformation of the variogram or the variogram model. I have 2 ref.
    Message 1 of 8 , Nov 28, 2003
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      Dear list members,


      I would like to know if anyone has information or bibliography on
      backtransformation of the variogram or the variogram model.
      I have 2 ref. only (Armstrong and Guiblin et al. 1995).
      Is this supose to give similar results to the log-normal kriging?
      Could anyone point me bibliography for this, please....

      Thank you very much in advance,
      any help would be appreciated,
      Best wishes
      Marta


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    • Edzer J. Pebesma
      ... I don t know if something is wrong. Maybe your data don t exhibit much spatial correlation, maybe they are so skew that without transformation you just
      Message 2 of 8 , Nov 28, 2003
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        Marcelo Alexandre Bruno wrote:

        >Whats wrong? Someone could help me?
        >
        I don't know if something is wrong. Maybe your data don't exhibit
        much spatial correlation, maybe they are so skew that without
        transformation you just don't see any in sample variograms.

        >The family of distribution of M. stehmanni is binomial
        >negative, is possible define these prior to variogram
        >and then result better variograms?
        >
        Not without modifying the source code. You could, as a first shot, try
        to look at Pearson residual variograms assuming a Poisson distribution.
        This can be done with gstat (be it a little forceful); look at the variance
        argument to the gstat function, define beta and the covariates such that
        the trend value is set for each observation. (If the trend is constant, this
        whole action is useless, given where you are now).
        --
        Edzer


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      • Brian R Gray
        you could modify the suggested approach by using a generalization of the Poisson, the neg binomial assumption you mention. most stat software allows negative
        Message 3 of 8 , Nov 28, 2003
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          you could modify the suggested approach by using a generalization of the
          Poisson, the neg binomial assumption you mention. most stat software
          allows negative binomial regression. in this case, the variance component
          of the Chi-squared resids may be better approximated (than under the
          Poisson assumption). as an aside, you may have a zillion zeroes with your
          fisheries data. such data may be handled moderately well by the neg bin
          assumption you mention. however, they may better be handled under the
          assumption that some portion of the zeroes are structural (ie *can't*
          generate a positive count) rather than stochastic. I haven't seen spatial
          corr assessed under these assumptions in the published lit. regardless,
          such "zero inflated" models are often considerably more complicated and may
          not suit your purposes. brian

          ****************************************************************
          Brian Gray
          USGS Upper Midwest Environmental Sciences Center
          2630 Fanta Reed Road, La Crosse, WI 54602
          608-783-7550 ext 19 - Onalaska campus or
          608-781-6234 - La Crosse campus
          fax 608-783-8058
          brgray@...
          *****************************************************************


          |---------+---------------------------->
          | | "Edzer J. |
          | | Pebesma" |
          | | <e.pebesma@geog.u|
          | | u.nl> |
          | | Sent by: |
          | | ai-geostats-list@|
          | | unil.ch |
          | | |
          | | |
          | | 11/28/2003 09:44 |
          | | AM |
          | | Please respond to|
          | | "Edzer J. |
          | | Pebesma" |
          | | |
          |---------+---------------------------->
          >--------------------------------------------------------------------------------------------------------------|
          | |
          | To: Marcelo Alexandre Bruno <marcelo2lei@...> |
          | cc: ai-geostats@... |
          | Subject: Re: AI-GEOSTATS: About gstat and binomial negative family data |
          >--------------------------------------------------------------------------------------------------------------|




          Marcelo Alexandre Bruno wrote:

          >Whats wrong? Someone could help me?
          >
          I don't know if something is wrong. Maybe your data don't exhibit
          much spatial correlation, maybe they are so skew that without
          transformation you just don't see any in sample variograms.

          >The family of distribution of M. stehmanni is binomial
          >negative, is possible define these prior to variogram
          >and then result better variograms?
          >
          Not without modifying the source code. You could, as a first shot, try
          to look at Pearson residual variograms assuming a Poisson distribution.
          This can be done with gstat (be it a little forceful); look at the variance
          argument to the gstat function, define beta and the covariates such that
          the trend value is set for each observation. (If the trend is constant,
          this
          whole action is useless, given where you are now).
          --
          Edzer


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        • Edzer J. Pebesma
          I know of a paper where people split up the process in begin zero or positive (binomial), and the value of the process given that it is positive (Poisson). In
          Message 4 of 8 , Nov 29, 2003
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            I know of a paper where people split up the process in begin zero or
            positive (binomial), and the value of the process given that it is
            positive (Poisson). In fact you're working with a composite pdf, two spatial
            processes that have to be merged later on. The idea is attractive,
            but not very easy. If you want the title of the paper, email me.
            --
            Edzer

            Brian R Gray wrote:

            >you could modify the suggested approach by using a generalization of the
            >Poisson, the neg binomial assumption you mention. most stat software
            >allows negative binomial regression. in this case, the variance component
            >of the Chi-squared resids may be better approximated (than under the
            >Poisson assumption). as an aside, you may have a zillion zeroes with your
            >fisheries data. such data may be handled moderately well by the neg bin
            >assumption you mention. however, they may better be handled under the
            >assumption that some portion of the zeroes are structural (ie *can't*
            >generate a positive count) rather than stochastic. I haven't seen spatial
            >corr assessed under these assumptions in the published lit. regardless,
            >such "zero inflated" models are often considerably more complicated and may
            >not suit your purposes. brian
            >
            >****************************************************************
            >
            >



            --
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          • Brian R Gray
            sounds like you are describing a two-part or hurdle model. a possibly more attractive but complex approach (zero-inflated count distributions) postulates two
            Message 5 of 8 , Dec 1, 2003
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              sounds like you are describing a two-part or hurdle model. a possibly more
              attractive but complex approach (zero-inflated count distributions)
              postulates two sources of zeroes: structural and stochastic. this doesn't
              require working with a zero-truncated count distribution. the downside is
              that the process defining how zeroes are separated is latent. brian

              ****************************************************************
              Brian Gray, Ph.D.
              USGS Upper Midwest Environmental Sciences Center
              2630 Fanta Reed Road, La Crosse, WI 54602
              608-783-7550 ext 19 - Onalaska campus or
              608-781-6234 - La Crosse campus
              fax 608-783-8058
              brgray@...
              *****************************************************************


              |---------+---------------------------->
              | | "Edzer J. |
              | | Pebesma" |
              | | <e.pebesma@geog.u|
              | | u.nl> |
              | | |
              | | 11/29/2003 06:35 |
              | | AM |
              | | |
              |---------+---------------------------->
              >--------------------------------------------------------------------------------------------------------------------------------------------------|
              | |
              | To: Brian R Gray <brgray@...> |
              | cc: ai-geostats@..., Marcelo Alexandre Bruno <marcelo2lei@...> |
              | Subject: Re: AI-GEOSTATS: About gstat and binomial negative family data |
              >--------------------------------------------------------------------------------------------------------------------------------------------------|




              I know of a paper where people split up the process in begin zero or
              positive (binomial), and the value of the process given that it is
              positive (Poisson). In fact you're working with a composite pdf, two
              spatial
              processes that have to be merged later on. The idea is attractive,
              but not very easy. If you want the title of the paper, email me.
              --
              Edzer

              Brian R Gray wrote:

              >you could modify the suggested approach by using a generalization of the
              >Poisson, the neg binomial assumption you mention. most stat software
              >allows negative binomial regression. in this case, the variance component
              >of the Chi-squared resids may be better approximated (than under the
              >Poisson assumption). as an aside, you may have a zillion zeroes with your
              >fisheries data. such data may be handled moderately well by the neg bin
              >assumption you mention. however, they may better be handled under the
              >assumption that some portion of the zeroes are structural (ie *can't*
              >generate a positive count) rather than stochastic. I haven't seen spatial
              >corr assessed under these assumptions in the published lit. regardless,
              >such "zero inflated" models are often considerably more complicated and
              may
              >not suit your purposes. brian
              >
              >****************************************************************
              >
              >








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            • Steven Citron-Pousty
              If you can handle writing (or get someone to write) MCMC code/bayesian work then take a look a this zero inflated poisson with spatial effects. Zero-Inflated
              Message 6 of 8 , Dec 1, 2003
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                If you can handle writing (or get someone to write) MCMC code/bayesian
                work then take a look a this zero inflated poisson with spatial effects.
                Zero-Inflated Models with Application to Spatial Count Data
                D.K.Agarwal,A.E.Gelfand and S.Citron-Pousty, Environmental and
                Ecological Statistics 2002, vol 9, pp 341-355
                Zero inflated poisson comes awfully close to a negative binomial, and
                makes more "biological" sense (i.e. there are a lot more places with
                zero counts and there is a process to account for them).
                Thanks,
                Steve

                Brian R Gray wrote:

                >sounds like you are describing a two-part or hurdle model. a possibly more
                >attractive but complex approach (zero-inflated count distributions)
                >postulates two sources of zeroes: structural and stochastic. this doesn't
                >require working with a zero-truncated count distribution. the downside is
                >that the process defining how zeroes are separated is latent. brian
                >
                >****************************************************************
                >Brian Gray, Ph.D.
                >USGS Upper Midwest Environmental Sciences Center
                >2630 Fanta Reed Road, La Crosse, WI 54602
                >608-783-7550 ext 19 - Onalaska campus or
                >608-781-6234 - La Crosse campus
                >fax 608-783-8058
                >brgray@...
                >*****************************************************************
                >
                >
                >|---------+---------------------------->
                >| | "Edzer J. |
                >| | Pebesma" |
                >| | <e.pebesma@geog.u|
                >| | u.nl> |
                >| | |
                >| | 11/29/2003 06:35 |
                >| | AM |
                >| | |
                >|---------+---------------------------->
                > >--------------------------------------------------------------------------------------------------------------------------------------------------|
                > | |
                > | To: Brian R Gray <brgray@...> |
                > | cc: ai-geostats@..., Marcelo Alexandre Bruno <marcelo2lei@...> |
                > | Subject: Re: AI-GEOSTATS: About gstat and binomial negative family data |
                > >--------------------------------------------------------------------------------------------------------------------------------------------------|
                >
                >
                >
                >
                >I know of a paper where people split up the process in begin zero or
                >positive (binomial), and the value of the process given that it is
                >positive (Poisson). In fact you're working with a composite pdf, two
                >spatial
                >processes that have to be merged later on. The idea is attractive,
                >but not very easy. If you want the title of the paper, email me.
                >--
                >Edzer
                >
                >Brian R Gray wrote:
                >
                >
                >
                >>you could modify the suggested approach by using a generalization of the
                >>Poisson, the neg binomial assumption you mention. most stat software
                >>allows negative binomial regression. in this case, the variance component
                >>of the Chi-squared resids may be better approximated (than under the
                >>Poisson assumption). as an aside, you may have a zillion zeroes with your
                >>fisheries data. such data may be handled moderately well by the neg bin
                >>assumption you mention. however, they may better be handled under the
                >>assumption that some portion of the zeroes are structural (ie *can't*
                >>generate a positive count) rather than stochastic. I haven't seen spatial
                >>corr assessed under these assumptions in the published lit. regardless,
                >>such "zero inflated" models are often considerably more complicated and
                >>
                >>
                >may
                >
                >
                >>not suit your purposes. brian
                >>
                >>****************************************************************
                >>
                >>
                >>
                >>
                >
                >
                >
                >
                >
                >
                >
                >
                >--
                >* 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
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                >
                >


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              • Marta Rufino
                Here are the answers I had for my question: Thank you very much. Marta ... -- * To post a message to the list, send it to ai-geostats@unil.ch * As a general
                Message 7 of 8 , Dec 2, 2003
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                  Here are the answers I had for my question:
                  Thank you very much.
                  Marta



                  >>Dear list members,
                  >>
                  >>
                  >>I would like to know if anyone has information or bibliography on
                  >>backtransformation of the variogram or the variogram model.
                  >>I have 2 ref. only (Armstrong and Guiblin et al. 1995).
                  >>Is this supose to give similar results to the log-normal kriging?
                  >>Could anyone point me bibliography for this, please....
                  >>
                  >>Thank you very much in advance,
                  >>any help would be appreciated,
                  >>Best wishes
                  >>Marta


                  Carme Hervada i Sala <carme.hervada@...> :
                  >It depends on the transformation you do!
                  >See GSLIB- book (1992) for normal score transform
                  >see
                  >G. Mateu-Figueras et al: Normal in R+ vs lognormal in R., 2002 (iamg
                  >meeting, berlin september 2002)



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