---------------------------------------------------------------------- Message: 1 Date: Thu, 13 Mar 2014 23:50:47 -0500 From: Eliot Miller <eliotmil...@umsl.edu> To: r-sig-ecology@r-project.org Subject: [R-sig-eco] Comparing results of two CCAs Message-ID: <CAKcGSHJYCsnoN3WT8J3f0rB=gywqk_jyfnkvb5wstpkhkys...@mail.gmail.com> Content-Type: text/plain 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.
variance partitioning using CCA is what you need. Kind regards, Alain Zuur
-- Dr. Alain F. Zuur First author of: 1. Beginner's Guide to GAMM with R (2014). 2. Beginner's Guide to GLM and GLMM with R (2013). 3. Begginner's Guide to GAM with R (2012). 4. Zero Inflated Models and GLMM with R (2012). 5. A Beginner's Guide to R (2009). 6. Mixed effects models and extensions in ecology with R (2009). 7. Analysing Ecological Data (2007). Highland Statistics Ltd. 9 St Clair Wynd UK - AB41 6DZ Newburgh Tel: 0044 1358 788177 Email: highs...@highstat.com URL: www.highstat.com _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology