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

[Rajendra Mohan panda]

Dear Prof David Warton

Thanks a lot for your nice introspection on my data. I appreciate your
valuable comments. I am also trying to explore gamm or VGAM to match its
suitability with data. Its fine. However, I am thinking to reduce my data
structure by removing some of the species showing interspecific
correlation. Honestly speaking I do not have thought of it. Can you please
give more insights regarding this (interspecies correlation). I am also
interested in studying species-environment relationship (not by CCA or RDA).

Your kind comments are highly appreciated.


With Best Regards
Rajendra M Panda
School of Water Resources
Indian Institute of Technology Kharagpur, India

On Wed, Feb 18, 2015 at 4:36 AM, David Warton <david.warton at unsw.edu.au>
wrote:

> Hi Rajendra and Greg,
> A couple of quick thoughts:
>
> Firstly, Rajendra the method that is applicable to your data really
> depends on the research question - what is it that you are trying to
> achieve.  It is always hard to offer help on what analysis method is suited
> to a question without knowing the original research objective.  The gamm
> function for example might be useful to you if you are primarily interested
> in predictive modelling, and also if you think that you have a common
> nonlinear response to environmental variables with some "noise" around this
> pattern for different spp (which can be represented as random effects).
> You could alternatively use this function to fit a separate smoother for
> each spp but that would be a pretty complicated model and few would have
> sufficient data to justify that level of model complexity.  VGAM y Thomas
> Yee offers and option in between these two.
>
> Secondly, something you need to worry about with this type of data is
> interspecies correlation - for various reasons (including species
> interaction), it is widely thought and even better often observed that
> species are correlated in abundance (or presence/absence, whatever) even
> after accounting for environmental predictors.  This makes the problem
> multivariate.  If you care about making joint inferences across species and
> you don't account for correlation between species you can get things quite
> wrong.  The gamm function I think could handle residual correlation, but
> not the way you specified it, and it would have a lot of trouble, unless
> you have only a handful of species and quite decent abundance data on
> each.  On the other hand if you are just making predictions separately for
> each spp then you don't need to worry too much about this.
>
> All the best
> David
>
>
> David Warton
> Professor and Australian Research Council Future Fellow
> School of Mathematics and Statistics and the Evolution & Ecology Research
> Centre
> The University of New South Wales NSW 2052 AUSTRALIA
> phone (61)(2) 9385-7031
> fax (61)(2) 9385-7123
>
> http://www.eco-stats.unsw.edu.au/ecostats15.html
>
>
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