Hi,

I have just posted the same question on the general R help mailing list,
but thought that this list might be more appropriate. I am a new user of
R.

Here is my problem:
I have 58 sites from across South America. I have done a regression
analysis to relate environmental and biogeographical variables to
species richness and want to test whether my residuals are
autocorrelated. As far as I understand the Moran's I, I have to take all
possible combinations between all points into account to test this. So I
have used dnearneigh() with the lower boundary set to 0 and the upper
boundary set arbitrarily high to make sure all connections are included.


>coords <- as.matrix(cbind(lowland$long, lowland$lat))
>coord.nb <- dnearneigh(coords, 0, 10000, longlat=TRUE)
>coord.list <- nb2listw(coord.nb, style="W")
>lianasp.lm <- lm(lianasprich ~ log(averdist) + dsl + lianadens +
wooddens)
>lm.morantest(lianasp.lm, coord.list, alternative="two.sided")

However, this gives me a Moran's I which is exactly the same as the
expected Moran's I (and hence a p-value of 1). If I change the lower or
upper boundary slightly so that not all possible links are taken into
account, the value is different, but still really near to the expected
Moran's I. I don't understand why these values are or the same or nearly
so.

I am new to spatial statistics, so this might me a really basic question
and my appologies if it is, but I am generally a bit at a loss now about
the Moran's I and I am wondering if I have calculated it right. Have
used to right method to convert my coordinates into neighbourhood
distances (and if not, which method should I have used) and am I
understanding and calculation the Moran's I correctly?

Any help would be greatly appreciated.

Many thanks,
Geertje

~~~~
Geertje van der Heijden
PhD student
Tropical Ecology
School of Geography
University of Leeds
Leeds LS2 9JT

Tel: (+44)(0)113 3433345 
Email: g.m.f.vanderheijden04@leeds.ac.uk



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