This is a stimulating point. testing correlation with autocorrelated data. The reply by Roger is great! You could test additionally bootstrap methods (rely on Brian Cade's papers). I guess that it does not solve the autocorrelation problem actually, but ....
Further, Ingolf Kuhn has developed a number of measures of autocorrelation. They are available in R. Just take a look at his home page. Best! Duccio > > Great, this worked, thank you for your quick reply Roger. The missing > values > > are intended - I didn't realise that I had to specify such to R. > > > > > > This probably going to show my vast ignorance of R in comparison to SPSS, > but > > is there a way to tell / set the significance level for the analysis? > > ?cor.test > > but beware that your observations are not independent, and will have > inflated > significance levels, so the test output really should not be used unless > you > can first show that there is no spatial autocorrelation in either variable. > You > could look at ?Geary in the raster package as a way of checking > autocorrelation. If you are an ecologist, you could look at Fortin & Dale > for > background, and maybe the Numerical ecology with R useR! book from > Springer. > The Moran() and Geary() functions in raster don't give you a significance > test > for autocorrelation, though, you may need other approaches in spdep for > that. > > Hope this helps, > > Roger > > > > > cheers, > > Rebecca > > > > > > > > >
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