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AI-GEOSTATS: transformation of data

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  • Sibylle Eisenberger
    I´m doing my diploma thesis on the spatial distribution of weeds and I´m an absolute beginner with geostatistics. Please take that into account when reading
    Message 1 of 2 , Apr 9, 2002
      I´m doing my diploma thesis on the spatial distribution of weeds and I´m an absolute beginner with geostatistics. Please take that into account when reading my question.

      My data are weed counts with excess zeros and fit a negative binomial distribution. But as far as I know semivariagram modelling can only be done with a more or less gaussian distribution. If yes, has anybody an idea how to transform negative binomial data to get a gaussian distribution? I would be very pleased if anybody of you could give me at least a tip how to solve this problem or maybe you can recommend some literature.


      Thanks a lot in advance.

      Regards,
      Sibylle



      [Non-text portions of this message have been removed]
    • Edzer J. Pebesma
      Dear Sibylle, I suspect your residuals will never become normal, because your data are counts. Luckily, normality is not a requirement for variogram
      Message 2 of 2 , Apr 9, 2002
        Dear Sibylle,

        I suspect your residuals will never become normal, because your data
        are counts. Luckily, normality is not a requirement for variogram
        calculation nor for kriging interpolation.

        However, before calculating variograms it may be a good idea to
        correct for non-stationarity in the variances, and work with Pearson
        residuals.

        See:

        Gotway, C.A., Stroup, W.W. (1997) A Generalized Linear Model Approach
        to Spatial Data Analysis and Prediction. Journal of Agricultural, Biological
        and Environmental Statistics 2(2), pp. 157--178.

        Diggle, P.J., Liang, K-Y., Zeger, S.L. (1994) Analysis of Longitudinal
        Data. Oxford University Press, Oxford.

        or the more advanced approach of:

        Diggle, P.J., J.A. Tawn, R.A. Moyeed (1998), Model-based
        geostatistics. Applied Statistics 47(3), pp 299-350.
        --
        Edzer

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