[R-sig-eco] Removing non significant response variable in rda analysis with forward selection?

[Gavin Simpson]

On Fri, 2010-07-30 at 10:11 +1200, Etienne Laliberté wrote:
> Dear Amélie,
> 
> 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:
> 
> Dufrêne, 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 à 08:00 -0700, amelie_can a écrit :
> > 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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