AI-GEOSTATS: answers to "transformation of data"

[Sibylle Eisenberger]

Hi!   Please find below my original message and the list of answers to my question concering the transformation of negative binomial data deriving from weed counts. Thanks everybody for your effort!   Sibylle   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 I suggest you may want to transform the data in a different way, namely by recording as a rate per something such as area, i.e. to make the data look like averages over cells. Presumably your counts already represent something like this but the problem with pure counts is that they don't "add" right. The variogram corresponds to "point" data and the theory provides a way to "regularize" the variogram when the support changes, pure counts will likely not behave properly in that respect. With respect to transforming a negative binomial to a normal, strictly speaking that can't be done since the negative binomial is discrete and the normal is continuous. You might want to look at some of the literature relating to geostatistics and entomology, see for example papers by Liebhold and Hohn. Donald E. Myers http://www.u.arizona.edu/~donaldm Hi,   just a quick suggestion. If you have enough data you could use a non-parametric indicator kriging technique. Often when there are many zeros this works well to delineate the regions of presence and absence.   Ben   Benjamin Warr Research Associate Centre for the Management of Environmental Resource(CMER) INSEAD Boulevard de Constance, 77305 Fontainebleau Cedex, France Tel: 33 (0)1 60 72 4456 Fax: 33 (0)1 60 74 55 64 e-mail: [EMAIL PROTECTED] http://www.insead.fr/CMER Sibylle, I am rather new on geostats too, and I went through the same problem when I started.  I work with soybean cyst nematode, so I also have count data with a negative binomial distribution. What I did with my data is a log 10(counts+1) transformation.  Then you do the semivariogram and if it is not stationary, try to remove a linear or a quadratic trend.  If that solves the non-stationarity problem, then apply universal krigging. It is pretty simple to do with Surfer. Good luck! Felicitas Hy Sibylle   You can fit variogram models for any kind of distribution. Gaussian distributions are required just on some simulation algorithms, but gaussian transformation (or gaussian anamorphosis) is a useful tool to use and transform a raw variable in a gaussian variable, with mean = 0 and variance = 1, making structures more clear on variography.   For that you can use gslib (normal score transformation or nscore.par) or gaussian anamorphosis at Isatis Software (Geovariances).     Alessandro Henrique Medeiros Silva Geologist - Anglogold Brasil [EMAIL PROTECTED]   +55-31-3589-1687 +55-31-9953-0759     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   Just as a complement to Edzer's email: The package geoRglm (www.maths.lancs.ac.uk/~christen) does the analysis based on the Poison/Binomial  models suggested in his last reference. geoRglm in an add-on (package) to the R software (www.r-project.org) Cheers P.J. Paulo Justiniano Ribeiro Jr Departamento de Estatistica Universidade Federal do Parana' Caixa Postal 19.081 CEP 81.531-990 Curitiba, PR  -  Brasil e-mail: [EMAIL PROTECTED] http://www.maths.lancs.ac.uk/~ribeiro (english) http://www.est.ufpr.br/~ribeiro (portugues)     Hi Sybille, You may want to look at using an indicator transformation for your data. I.e. split the distribution into (ordered) intervals (say 5 ore more..but it will depend on your data)...and code your variable as 1 if it is less than the interval-threshold, 0 if it is not. So you get a 'categorical' data set. Zero could be one of the thresholds. You would then use indicator kriging to interpolate. This is usually more flexible and does not use a gaussian model. I hope it helps, Alessandro Gimona Fisheries Research Services Aberdeen Scotland UK