Re: AI-GEOSTATS: answers to "transformation of data"
- Hi Sibylle:
Sorry this is so late, but I have just been working on generating a negative
binomial as described by Pielou, Cressie and others (e.g., Diggle,
Ripley). Apparently it can be derived in two ways, one of which is a
Poisson distribution of clusters (of weeds) and a gamma distribution
describing the number of individual weeds per cluster.
You don't say what your objective is -- if you are interested in kriging,
do you want to interpolate to find weed patches that you missed during
sampling, generate other possible realizations, or you just want to find an
index of autocorrelation?
Because you are focusing on the semivariogram, I'm assuming its the latter
you want. The ratio of the variance to the mean (counts/quadrat) and
Ripley's K are two indices of contagion used to describe point processes.
The semivariogram is not the best tool to analyze your data with. I would
look in Cressie's book, Chapter 8 on Spatial point patterns. If you want
to generate alternative realizations or describe your distribution, one (or
more) of these can be fitted to your data.
At 09:07 AM 4/16/2002 +0200, you wrote:
>Hi![Non-text portions of this message have been removed]
>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!
>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
>Thanks a lot in advance.