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GEOSTATS: logistic regression with spatially autocorrelated data-Answers

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  • Kris Verheyen
    Dear, Herewith a summary of the answers and comments on my question about logistic regression (see below for the original message). Overall, it seems that many
    Message 1 of 1 , Dec 15, 1999
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      Dear,

      Herewith a summary of the answers and comments on my question about logistic
      regression (see below for the original message).


      Overall, it seems that many people are working with logistic regression of
      spatially autcorrelated data. However, the solutions for this problem are
      scarce and often very complex..


      A. To test for spatial autocorrelations of the regression residuals, A.
      Lister and A. Getis suggest the use of the Moran coefficient. This can be
      done with Spacestat for instance.
      Gilles Bourgault remarked that while fitting semivariograms of the
      standardized regression residuals - as I have done -, the nugget effect is
      often overestimated since there is a lack of measurements for the short lag
      distances (I' m working with a 20m x 20m grid). However, I don't think this
      is a problem since the only thing we want to do is to test if the samples we
      are working with are spatially autocorrelated. So, in my opinion, it doesn't
      matter if spatial autocorrelation is present at shorter lag distances!

      B. If the degree of spatial autocorrelation is much larger than can be
      explained from the spatial covariates (which means that the residuals are
      still spatially autocorrelated), then models should be build that properly
      account for both the spatial autocorrelation and the dependence on covariates
      (Huffer & Wu , 1998).
      In the following papers spatial autocorrelation is explicetely taken into
      account
      -Huffer & Wu (1998) Biometrics 54, 509-524. 'Markov Chain Monter Carlo for
      Autologistic Regression Models with application to the distribution of plant
      species' Fred Huffer has written a Fortran/S-plus program for this which is
      available on the website of Florida State University.
      -Heagerty & Lele (1998) Journal of the American Statistical Association
      93(443), 1099-1111. A composite likelihood approach to binary spatial data
      -Albert & McShane (1995) Biometrics. A generalized estimating equations
      approach for spatially correlated binary data
      -Chung & Agterberg (1980) Mathematical Geology. Regression models for
      estimating mineral resources from geological map data

      However, most of these methods are not straithforward and not easily
      applicable.


      C. In my study spatial autocorrelation of the residuals wasn't present
      anymore after fitting of the logistic regression model. So I was lucky!!!!


      Best regards,

      Kris


      Original message :

      "In a +/- 25ha forest, I've mapped the forest ground flora on the basis of a
      20m x 20m grid (a total of > 700 grid cells). Quite logically, the
      distribution of the mapped plant species is not random, but exibits a high
      degree of spatial autocorrelation.
      In order to explain the spatial distributions of these plant species, I
      performed a logistic regression with the species presence/absence data and pH,

      soil type, illuminance, ... as indipendent variables.

      To check for spatial autocorrelation I calculated semivariograms for the
      standardized
      residuals of the logistic regressions.

      Is the applied method correct and are my conclusions justified?"



      Laboratory for forest, nature and landscape research
      Vital Decosterstraat 102, B-3000 Leuven
      tel. : +32-16 329737 fax. : +32-16 329760
      e-mail : kris.verheyen@...
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