The partial least squares sounds really promising, thanks. I now need to go
read about and try some tests with it. If you or anyone else has a
preferred implementation of this in R I'd be interested in hearing about it!

Alain--can you elaborate on how I might be able to use variance
partitioning? I haven't used either of these methods before, but reading
about it it sounds intended to quantify the amounts of variation in a
single matrix explained by multiple matrices. I'm probably missing
something. If you could explain more I'd be very interested.

Thanks for your help!
Eliot


On Fri, Mar 14, 2014 at 2:16 AM, Paolo Piras <paolo.piras@uniroma3.it>wrote:

> Hi,
> maybe partial least squares:
>
> you can run two separate partial least squares analyses and then comparing
> vectors.
>
> best
> paolo
> ________________________________________
> Da: r-sig-ecology-bounces@r-project.org <
> r-sig-ecology-bounces@r-project.org> per conto di Eliot Miller <
> eliotmiller@umsl.edu>
> Inviato: venerdė 14 marzo 2014 05.50
> A: r-sig-ecology@r-project.org
> Oggetto: [R-sig-eco] Comparing results of two CCAs
>
> I have four datasets: morphological measurements for a set of species (M1),
> ecological measurements for the same set of species (E1), morphological
> measurements for a second set of species (M2), and ecological measurements
> for this second set of species (E2).
>
> I am interested in finding the linear combinations of variables between M1
> and E1, and between M2 and E2. That is, I'd like to know what combinations
> of morphological measurements are associated with what combination of
> ecological measurements--for each set of species separately. This seems
> like a good use of CCA (two separate CCAs).
>
> But here's where things get tricky for me. I'd like to see whether the same
> linear combinations from one set of species do a good job of explaining the
> variation in the second set of matrices. And I'd like to see how they
> differ, if possible...e.g. yes the canonical function from the first CCA
> does explain some of the variation in the second, but a different function
> could do a lot better.
>
> Is this making any sense? I could see simply running these as two separate
> CCAs, then comparing the results qualitatively. But that doesn't seem very
> rigorous. Should I be considering some other approach entirely?
>
> Thanks for any input!
>
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