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

[Gavin Simpson]

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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> >
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-- 
Gavin Simpson, PhD

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