[R] RDA and trend surface regression

[Stéphane Dray]

Dear All,

PCNM and more general MEM can be found in the package spacemakeR
available here (http://biomserv.univ-lyon1.fr/~dray/). As said by Jari,
I do not want to put the package on CRAN. I have no no problems to
include it in another package (vegan or spdep). It has a vignette which
can be interesting for spdep.  However, I have some problems with this
vector approach for selection of variables in multivariate analysis.
spdep has some function to select these eigenvectors in univariate
regression, we have to find some rules for selecting in the multivariate
case. I use AIC for the paper but it is quite sure that it is not
correct (see comments of Jari in the vegan help page).
multispati is a spatially constrained multivariate analysis. I think
that this approach is more clean in a 'statistical sense' but quite
different: we do not have variance but autocorrelation and so negative
eigenvalues in multivariate analysis !

I have see that Roger has send an announce for a spatial-R meeting in
R-Geo List. I am interested to participate to the Bergen session (I love
rain ;-) ). It could be nice to speak about this subject in Bergen.


Cheers,




Jari Oksanen wrote:
> On 27 Feb 2007, at 20:55, Gavin Simpson wrote:
>
>   
>> On Tue, 2007-02-27 at 13:13 -0500, Kuhn, Max wrote:
>>     
>>> Helene,
>>>
>>> My point was only that RDA may fit a quadratic model for the terms
>>> specified in your model. The terms that you had specified were already
>>> higher order polynomials (some cubic). So a QDA classifier with the
>>> model terms that you specified my be a fifth order polynomial in the
>>> original data. I don't know the reference you cite or even the
>>> subject-matter specifics. I'm just a simple cave man (for you SNL 
>>> fans).
>>> But I do know that there are more reliable ways to get nonlinear
>>> classification boundaries than using x^5.
>>>       
>> I doubt that Helene is trying to do a classification - unless you
>> consider classification to mean that all rows/samples are in different
>> groups (i.e. n samples therefore n groups) - which is how RDA
>> (Redundancy Analysis) is used in ecology.
>>
>> You could take a look at multispati in package ade4 for a different way
>> to handle spatial constraints. There is also the principle coordinates
>> analysis of neighbour matrices (PCNM) method - not sure this is coded
>> anywhere in R yet though. Here are two references that may be useful:
>>
>>     
> Stéphane Dray has R code for finding PCNM matrices. Google for his 
> name: it's not that common. I also have a copy of his function and can 
> send it if really needed, but it may be better to check Dray's page 
> first. Stéphane Dray says think that not all functions need be in CRAN. 
> May be true, but I think it might help many people.
>
> There are at least three reasons why not use polynomial constraints in 
> RDA. Max Kuhn mentioned one: polynomials typically flip wildly at 
> margins (or they are unstable in more neutral speech). Second reason is 
> that they are almost impossible to interpret in ordination display. The 
> third reason is that RDA (or CCA) avoid some ordination artefacts 
> (curvature, horseshoe, arc etc.) just because the constraints are 
> linear: allowing them to be curved allows curved solutions. These 
> arguments are not necessarily valid if you only want to have variance 
> partitioning, or if you use polynomial conditions ("partial out" 
> polynomial effects in Canoco language). In that case it may make sense 
> to use quadratic (or polynomial) constraints or conditions.
>
> cheers, Jari Oksanen
>
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>   


-- 
Stéphane DRAY (dray at biomserv.univ-lyon1.fr )
Laboratoire BBE-CNRS-UMR-5558, Univ. C. Bernard - Lyon I
43, Bd du 11 Novembre 1918, 69622 Villeurbanne Cedex, France
Tel: 33 4 72 43 27 57       Fax: 33 4 72 43 13 88
http://biomserv.univ-lyon1.fr/~dray/