All, How can I save (rename as a vector?) the "where" output of plot
assignment to groups from mvpart() to use as input to mrpp of the same data?
Mike Marsh
On 9/10/2015 3:00 AM, r-sig-ecology-requ...@r-project.org wrote:
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Today's Topics:
1. How to incorporate spatial autocorrelation in multivariate
GLM (Alexandre F. Souza)
2. Re: How to incorporate spatial autocorrelation in
multivariate GLM (Tim Meehan)
----------------------------------------------------------------------
Message: 1
Date: Wed, 9 Sep 2015 09:25:12 -0300
From: "Alexandre F. Souza" <alexsouza.cb.ufrn...@gmail.com>
To: Lista de discussao R-sig-ecology <r-sig-ecology@r-project.org>
Subject: [R-sig-eco] How to incorporate spatial autocorrelation in
multivariate GLM
Message-ID:
<cafxqfgnvhv5gca8porcnw96w__acpoak4yc59sz+akxrhxy...@mail.gmail.com>
Content-Type: text/plain; charset="UTF-8"
Dear friends,
I would like to ask for some advice.
I am embarking in the analysis of species occurrence date across
biogeographic scales in South America. I am willing to try to jump from
more traditional distance-based multivariate analysis (e.g., RDA on
hellinger-transformed abundance data) to multivariate GLM as proposed by
Warton (mvabund package) and also by Yee (VGAM package).
However, distance-based methods have grown to incorporate spatial
dependency through the development of MEM and AEM techniques, which model
symmetric and asymmetric spatial relationships and can be included in the
explanatory side of the analysis.
Reading the multivariate GLM papers, however, I have not seen clear mention
on how to control or include spatial autocorrelation. I am thinking of
including MEM and perhaps AEM variables simply as co-variables added to the
explanatory environmental variables in the multivariate GLM.
Is this a step I will regret later on?
Thanks in advance for any thoughts,
All the best,
Alexandre
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