On Fri, 2010-07-30 at 10:11 +1200, Etienne Lalibert? wrote:
Dear Am?lie, To me, the approach you're describing sounds like you're trying to shoehorn you data to fit your predictions, which can be dangerous at best and dishonest at worst. My understanding is that your explanatory variable is a factor with different groups. If you're interested to see which species best discriminate between these a priori specified groups, then you may want to use canonical discriminant analysis (CAD). Have a look at: Anderson, M. J., and T. J. Willis. 2003. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84:511-525. I've only used this in PRIMER v6 / PERMANOVA, but not in R. However I believe it is implemented in: ?capscale
capscale() is like cca() but without the constraint of using the chi-square metric (or rda() without Euclidean). It still takes a species response matrix to be predicted by a set of explanatory variables and these are fitted as linear combinations, just as in cca().
but Jari and others will be more helpful there. A somewhat related (but focusing on a different question) approach could be the IndVal method described in: Dufr?ne, M., and P. Legendre. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecological Monographs 67:345-366. where you could look at which species are the best "indicators" that characterize different groups of sites.
Yes, I too think this might be a good way to go if the focus is on why species seem to be associated with which site-types. If the OP is interested in her species as the response, and/or wants a less cluttered plot, then something else will be required. G
Hope that helps, Etienne Le jeudi 29 juillet 2010 ? 08:00 -0700, amelie_can a ?crit :
Hello all, My problem is somewhat similar to Vit Syrovatka posted on July 23th and titled ?Species fit in ordination?. In my project, I am doing an rda between species abundances (response variable ? about 130 species) and type of sites (explanatory/environmental variable ? one variable). When I finish my analysis & plot it, I have a lot of species present and I suspected that several of them did not contribute significantly to the analysis. Consequently, I decided to do a forward selection analysis. Usually, a forward selection analysis is used to remove environmental variable that don?t relate as well with the response variable. But in my case, I only have one environmental variable, so I basically switch around my response variable (which are now my types of sites) and my explanatory variable (which is now my species abundances) for the forward selection analysis. So, basically, the forward selection shows me which species explains significantly the types of sites found. Then I reran my rda analysis to found that including the 20 species that were significant in the forward analysis would explain as much the variation of my rda axis as when I had all of my species. Is this correct? My supervisor raised question about the fact that I used my response variable in forward analysis instead of environmental variable?. ? If not, how can we remove species that are not significant? I thought of trying to find which species are correlated to one another. I know one can use the cor.test function or the vif function, but it is problematic to me, as we can only check two species per analysis. Since I have about 130 species, checking all of those permutations by hand is just too long. I also thought about doing a partial rda analysis, one species at the time to see its significance in the model, but again, seemed too long. Thank you all for your time, Amelie D?Astous Laval university Quebec
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