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AI-GEOSTATS: About gstat and binomial negative family data

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  • Marcelo Alexandre Bruno
    Dear members I m newer to geostatistics analysis. So, my work now is probe to my lab.chief that geostat. anal. is better than other analysis to create
    Message 1 of 8 , Nov 27, 2003
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      Dear members

      I'm newer to geostatistics analysis. So, my work now
      is probe to my lab.chief that geostat. anal. is better
      than other analysis to create probability maps from
      fishery acoustic data of surveys in Brazil. Then,
      generate kriging maps of probability distribution of
      Maurolicus stehmanni (fish sp).


      1)First step: using GMT, convert long, lat to linear
      projection
      [marcelo@localhost trab_R]$ mapproject file1.dat
      -R-53/-38/-35/-23 -Fn -Jm0/0/1c > file2.dat
      where R is region Fn to nautic miles JM is mercartor
      proj.
      2) using "R" gstats:
      compute variogram..., and points show no spatial
      dependence! Range is 0.5 nautical miles!
      3) selects limited areas, repeat step 1 and 2 and
      result is equal!
      4) remove nule values, repeat steps 1 and 2, and
      result is equal!
      5) change scale of projection, "-Jm0/0/10c", repeat
      steps 1 and 2, result is equal.
      Whats wrong? Someone could help me?
      The family of distribution of M. stehmanni is binomial
      negative, is possible define these prior to variogram
      and then result better variograms?
      My apologies for newbie questions, i'm very gratefully
      for this list!
      Marcelo
      Ps:someone research could contact me in PVT.

      =====
      ## ~~~~~~~ Oceanólogo ~~~~~~~ ##
      # Marcelo Alexandre Bruno
      # Linux User: 124592
      # Pós-graduação Oceanografia Biológica
      # FUNDACAO UNIV. FEDERAL do RIO GRANDE
      # Departamento de Oceanografia
      # Lab. de Tecnologia Pesqueira e Hidroacústica
      # AV. ITÁLIA km 8 s/n - CARREIROS
      # 96201-900 (0xx53) 2336528
      # Rio Grande - RS - BRAZIL
      ## ---------------------------------------- ##

      ______________________________________________________________________

      Yahoo! Mail: 6MB, anti-spam e antivírus gratuito! Crie sua conta agora:
      http://mail.yahoo.com.br

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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 2 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 3 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 4 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 5 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 6 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 7 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.
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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 8 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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