Using multiple species data for gam
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 ? 12:11, Gavin Simpson <ucfagls at gmail.com> a ?crit : 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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