Good day Clement, and R users,
I've used the hier.part package. I think that conceptually, if the form of
relationship with the dependent variable differs among independent variables,
hier.part won't provide unbiased results. Different samples from the same
population should yield the same est
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 s
That's a really great paper, but if memory serves, it focuses on
univariate regression models. Useful in this context for exploring the
responses of a single species at a time, instead of a multivariate
approach considering multiple species simultaneously.
By the way, I have the author as Dor
I would recomend the paper of Dortman et al.
(Ecography 30: 609628, 2007). This reviews many
available spatial statistical methods to take
spatial autocorrelation into account in tests of
statistical significance. From their abstract:
"Here, we describe six different statistical
approaches
Dear friends,
I found this thread very useful, so I wanted to apport something, Corrado,
you asked for some references about PCNM, here is what i found:
Borcard, D. and Legendre, P. 2002. All-scale spatial analysis of
ecological data by means of principal coordinates of neighbour
matrices. Ecolog
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 mome
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
Still another aproach would be Generalized Dissimilarity Modelling
[see Ferrier et al. in Diversity and Distributions (2007) 13:
252-264]. Basicallly, a MRM improved to account for non-linearity in
the data. From its webpage (http://www.biomaps.net.au/gdm/):"The only
version of the software cu
On Wed, 2009-10-14 at 09:13 +0200, Maarten de Groot wrote:
> Dear Jens,
>
> As far as I understood you are looking for the influence of one distance
> matrix on another. (Please correct me if I am wrong) Than the following
> reference might be useful:
>
> ter Braak, C. J. F. and Schaffers, A. P
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) wi
Dear R users,
In the hier.part package, hierarchical partitioning is built upon a GLM
(generalized linear model) framework to assess the independent and joint
effect from a set of predictors onto a single quantitative response
variable. In this context, the joint and independent effect from each f
Dear Jens,
As far as I understood you are looking for the influence of one distance
matrix on another. (Please correct me if I am wrong) Than the following
reference might be useful:
ter Braak, C. J. F. and Schaffers, A. P. 2004: Co-correspondence
analysis: a new ordination method to relate
Dear Sarah, Jari and Peter,
let me summarize what has been written so far
1) Jari said that: dbRDA needs rectangular data on right hand side of
the formula --> dist.matrix on RHS leads to a lack of independence *no
optimal solution*
2) Sarah suggests *MRM *
3) Peter suggests *Monmonier's maxi
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