[R-sig-eco] Using multiple species data for gam

[Mailing lissts]

Hi everyone ! 

In my humble and biased opinion, there are two approaches that may be interesting to considered to deal with so many species; the ordination approach and the model-based approach.

As Gavin proposed, using a CCA might not be a bad idea except for variance-mean problem highlighted by David Warton a few years ago in a paper in Methods in Ecology and Evolution (and can’t find it quickly at the moment). However, I worked on developing consensus RDA which might be helpful in dealing with this problem. If you want to take a look at the paper, it is here : http://dx.doi.org/10.1890/13-0648.1 <http://dx.doi.org/10.1890/13-0648.1>

Consensus RDA is currently implement in a package available on R-forge in the package ordiconsensus (https://r-forge.r-project.org/R/?group_id=68 <https://r-forge.r-project.org/R/?group_id=68>). 

Another approach that may be worth investigating for problems similar to the one discussed here was proposed by Ovaskainen and Soininen in Ecology in 2011 (http://dx.doi.org/10.1890/10-1251.1 <http://dx.doi.org/10.1890/10-1251.1>). I am currently working on implementing their work in a package called HMSC, which is also available on R-forge (https://r-forge.r-project.org/R/?group_id=1682 <https://r-forge.r-project.org/R/?group_id=1682>). Note that the HMSC package is not as mature and maybe a little buggy.

In any case, these two approaches are new ideas that might be interesting to consider in addition of the ones discussed in the current thread.

Have a good day !

Guillaume

> Le 2015-02-10 à 12:11, Gavin Simpson <ucfagls at gmail.com> a écrit :
> 
> mvabund has a manyany() function which allows you to run the same sort of
> analysis as manyglm() does without having to use a GLM. Hence you could do
> a many GAM using manyany() and the mgcv::gam() function(ality). There is an
> example of this on the ?manany help page.
> 
> Still, doing this for a 1000 species is going to be tough going, even if
> you just used manyglm() but it may be doable if you are prepared to wait
> for the models to fit and you have sufficient data in each species to fit a
> complex model like a GAM.
> 
> G
> 
> On 10 February 2015 at 10:28, Tim Meehan <tmeeha at gmail.com> wrote:
> 
>> If you want to do this in a glm framework, you might look into the mvabund
>> package:
>> 
>> http://cran.r-project.org/web/packages/mvabund/mvabund.pdf
>> 
>> I've never used it with anything approaching 1000 species, though.
>> 
>> On Tue, Feb 10, 2015 at 2:41 AM, Rajendra Mohan panda <
>> rmp.iit.kgp at gmail.com
>>> wrote:
>> 
>>> Dear All
>>> 
>>> I have >1000 species with presence and absence (0 or 1) values and with
>>> seven corresponding predictor variables. If I can run gam/glm for the
>> data
>>> using all species data simultaneously vs predictors. Data are arranged in
>>> columns against their GPS locations (see below). I know it is possible to
>>> do separately for each species.
>>> 
>>> Your kind response is highly appreciated.
>>> 
>>> Sites  Sp1  Sp2 Sp3 Alt Temp Pptn   Ft
>>> 1A         0      1    1     20   30     1000 Evergreen
>>> 
>>> With Best Regards
>>> Rajendra M Panda
>>> School of Water Resources
>>> Indian Institute of Technology Kharagpur, India
>>> 
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> 
> 
> 
> -- 
> Gavin Simpson, PhD
> 
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