Dear list - Apologies if this is not a repost. I sent it the first time in html format inadvertently and it hasn't shown up. Corrado - I meant to refer to a regression model - presumably you are going to build a regression model of sorts (although multivariate because of all the species) to see which of your explanatory variables is important. For some more informtion: Here is the website for SpaceMaker2, by Borcard and Legendre - they list a number of papers to describe the method. http://www.bio.umontreal.ca/casgrain/en/labo/spacemaker.html I also found this paper by Stephane Dray to be helpful. http://pbil.univ-lyon1.fr/members/dray/articles/SD163.php I must say, it is not all that simple to work out how to do the analysis. But the results are very interesting. M
Corrado wrote:
Dear Matthew, thanks for your kind answer! The first approach you describe is the one I have been looking at until now. I am puzzled about the second one: I do not really understand it. What model are you talking about, when you say "incorporate the spatial variation in the model"? At the moment I have no model, just the data and I am trying to reduce autocorrelation before analysing the data. Do you have any good reference (articles or books) about the approach you mention? Thanks in advance On Wednesday 14 October 2009 13:11:04 Matthew Landis wrote:
Corrado:
The simplest way would be to take a subset of sites to maximize the
distance between them. Say, choose 400 sites evenly spread over the
study area. That would minimize autocorrelation to the greatest extent
possible, but you would be throwing away data.
The second thing you could try would be to incorporate the spatial
variation in the model to control for it. This way you can also study
the autocorrelation, see what spatial scales it is operating and what it
looks like and try to learn something from it. Legendre, Borcard, Dray
and colleagues have developed some really interesting ways of dealing
with multivariate data and decomposing the variance into spatial
component vs. explanatory variables. I believe it is called PCNM and
can be found in the spacemakeR package (don't think it is on CRAN - have
to do a google search).
Good luck!
Matthew Landis
Corrado wrote:
Dear friends,
I have a large matrix of species (first 1100 columns) and environmental
variables (last 36 columns) for approx 2000 sites.
The distance between sites varies. Some sites are near to each other,
others are far.
I would like to select a subset of N sites (for example: 400 sites) with
the minimum spatial autocorrelation. The aim is to obtain a significant
number of sites to carry out some statistical analysis, but with spatial
autocorrelation significantly reduced.
Is there a procedure to do so in R? How would you approach the problem?
The aim of the "reduction" is to then work on dissimilarities between
sites that have the lowest possible spatial autocorrelation.
Thanks