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Re: AI-GEOSTATS: About gstat and binomial negative family data

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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 1 of 8 , Nov 28, 2003
      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 2 of 8 , Nov 29, 2003
        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 3 of 8 , Dec 1, 2003
          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 4 of 8 , Dec 1, 2003
            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 5 of 8 , Dec 2, 2003
              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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