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## AI-GEOSTATS: transformation of data

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• 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]
• 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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