Re: AI-GEOSTATS: data transformation and variograms
I think the problem might be even more subtle. Essentially you are looking
at a marked point process, and trying to apply methods designed
principally for data that is continuous throughout the sampling domain.
I would suggest looking at the following paper:
Stoyan and Waelder 2000. On variograms in point process statistics
II. Models of markings and ecological interpretation. Biometrical journal
Another approach you might think about is spatial cdf estimation. take a
look at the work of cressie and friends.
N Nicholas Lewin-Koh
/ \ Dept of Statistics
N----C C==O Program in Ecology and Evolutionary Biology
|| || | Iowa State University
|| || | Ames, IA 50011
CH C N--CH3 http://www.public.iastate.edu/~nlewin
\ / \ / nlewin@...
| || Currently
CH3 O Graphics Lab
School of Computing
National University of Singapore
The Real Part of Coffee kohnicho@...
On Thu, 19 Apr 2001, Juliann Aukema wrote:
> Hi. I have a question about transforming data.
> I have infection prevalence data for many points- a proportion of
> trees infected. Numbers are between 0 and 1. Sample size varies for the
> different points (because density of trees varies). When I plot a variogram
> of the prevalence data, I get a nice sill for about 4000 meters and then a
> rise in the variogram. If I take the residuals of prevalence against
> elevation the second rise goes away. Biologically this all makes sense and
> makes a nice story.
> However for some other analyses that I also did with this data, I
> was advised to logit transform the prevalence data because it is a
> proportion and should be binomially distributed.
> If I plot the variogram of the logit transformed prevalence, the
> first sill is much less distinct if it is there at all - this seems to be
> mostly due to one point, the last point before the rise, which now goes up
> instead of being about even with the previous point. ( I guess this
> difference is due to the stretching of zero prevalence values that occurs
> with the logit transformation.) And if I look at smaller lags, it looks
> like a power function with no sill. Biologically, that is harder to
> explain. If I plot the residuals of the (logit transformed prevalence)
> against ( elevation), the variogram has a nice sill and is similar, even
> prettier than the analysis of the untransformed data (but based on the
> previous variogram, I don't have a very good reason for plotting the
> My question, then is whether the logit transformation is necessary
> and/or appropriate for the geostatistical analysis. Does it make sense to
> use the transformed data for both variograms, for just the residuals
> (because the residuals are based on regression for which the transformation
> ought to be done) or for neither?
> Thank you very much.
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