This may be possible with an R packages but i don't know. What i've been
told is that a kind of analysis can be done with accupancy analysis and
this will give possibility to model the envioronmental factors that can
determine the presence of animal species. But this need a probabilitic
method.
good luck

2012/3/20 <r-sig-ecology-request@r-project.org>

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>   1. Using partykit::ctree with observations that are  spatially
>      autocorrelated (Kay Cichini)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 19 Mar 2012 18:04:59 +0100
> From: Kay Cichini <kay.cichini@gmail.com>
> To: r-sig-ecology@r-project.org
> Subject: [R-sig-eco] Using partykit::ctree with observations that are
>        spatially autocorrelated
> Message-ID:
>        <CADcQ+RoQEV8SFT7yCH-NRhfn8cV8d5WrON=NwtYGC2qob7fNTg@mail.gmail.com
> >
> Content-Type: text/plain
>
> Hello list members,
>
> I'm planning to model species presence / absence data (N = 523) that was
> collected over a geographic area. Samples come from preferential sites (sea
> level > 1200 m, obligatory presence of permanent waterbodies, etc). I want
> to infere on environmental factors determining the occurrence rate of
> several amphibian species.
>
> I want to use partykit::ctree because the method is intuitive and the
> output is very illustrative.  I want to use the resulting plots as decision
> trees for practitioners. Generally, I need to keep the analyses as simple
> as possible.
>
> May only problem is that I'm afraid that there is some amount of
> autocorrelation in my variables. I'm thinking about simply using a
> conservative minimal criterion - say, p > 0.9 or 0.8 and live with it -
> what do you think?
>
> Which tests for detecting spatial autocorrelation within dichotomous
> responses do you suggest?
>
> Or, can I consider the spatial correlation being irrelevant if I assume
> that it is allowed for by the covariates?
>
> Any ideas would be greatly appreciated!
>
> Thanks,
> Kay
>
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-- 
 Isidore

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