
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 currently available from this site consists 
of a set of functions for performing GDM within the R statistical 
software environment 
(<http://www.r-project.org/>http://www.r-project.org/) for the 
Windows platform."

HTH,

Marcelino


At 10:50 14/10/2009, Gavin Simpson wrote:
>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. 2004: Co-correspondence
> > analysis: a new ordination method to relate two community compositions.
> > Ecology 85, 834-846.
> >
> > I don't know whether this is implemented in R, though.
>
>It is, in the cocorresp package (by yours truly); although I think it
>fair to say that whilst the package will do the necessary computations,
>the associated functions for plotting ordination results and the like
>are somewhat lacking, very much so when compared to the offerings in
>packages like vegan and ade4. The code does little more than the Matlab
>code provided with the paper you cite.
>
> >From my reading of the thread and Jens' postings, Co-CA probably isn't
>quite the right method as it was specifically designed for two species
>matrices and uses an underlying unimodal response model like CA; you
>could think of Co-CA as a version of CCA that allows unlimited
>constraints and fits a non-linear (instead of linear) model in those
>constraints.
>
>Furthermore, Co-CA, and its implementation in cocorresp, requires the
>original data matrices on both sides of the model formula, not
>dissimilarity matrices.
>
>HTH
>
>G
>
> >
> > Kind regards,
> >
> > Maarten
> >
> > Sarah Goslee wrote:
> > > That doesn't make much sense to me. You'd need an entirely 
> different method
> > > than capscale.
> > >
> > > Perhaps what you're looking for is more like multiple 
> regression on distance
> > > matrices (implemented in MRM in ecodist)?
> > >
> > >      Lichstein, J. 2007. Multiple regression on distance matrices: A
> > >      multivariate spatial analysis tool. Plant Ecology 188: 117-131.
> > >
> > >      Legendre, P.; Lapointe, F. and Casgrain, P. 1994. Modeling brain
> > >      evolution from behavior: A permutational regression approach.
> > >      Evolution 48: 1487-1499.
> > >
> > > Sarah
> > >
> > > On Tue, Oct 13, 2009 at 11:13 AM, Jens Oldeland <oldeland@gmx.de> wrote:
> > >
> > >> Dear Sarah dear Jari,
> > >>
> > >> many thanks for your explanations. However, it wasnt what I 
> thought about,
> > >> sorry I definitely have to be more specific about the problem.
> > >>
> > >> Okay I try be more precise:
> > >>
> > >> the problem was that for example  capscale 
> accepts   "capscale(dist.matrix.1
> > >> ~ N + P + K *Ag, data=varechem)"
> > >> but I need  "capscale(dist.matrix.1 ~ dist.matrix.2, 
> data=dist.matrix.2)"
> > >>  so the trick was not on how to create a distance matrix but 
> how to use a
> > >> second on in a formula.
> > >>
> > >> We are trying a similar analysis like the the "distlm" program by Marti
> > >> Anderson does, however we had a problem with that and wanted to try the
> > >> analysis in R.
> > >>
> > >> thanks already for all your comments !
> > >>
> > >> best
> > >> Jens
> > >>
> > >
> > >
> > >
> > >
> >
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