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

[Gavin Simpson]

On Thu, 2010-07-29 at 08:00 -0700, amelie_can wrote:
> 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.

Smacks Head; To me, this makes no sense. Less than no sense actually. If
you are interested in the community of species, why do you want to
ignore most of them?

What you in effect did is a multiple regression with 130 predictor
variables. Unless you have LOTS (read lots and lots, and lots, and then
some more) of sites/samples you might as well just do:

spp.want <- sample(130, 20)

and used spp.want to select which species to retain.

The way you are talking it is as if you have the analysis the wrong way
round. You are analysing the species, therefore they cannot "contribute"
significantly or otherwise to the model. They are the things you are
trying to predict/model.

> 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?

If you can explain to me why you want to remove "not significant"
species then (and what "not significant" means in this case) maybe we
can try to help. If this is just because your plot is too crowded, then
I think you need to rethink what you are doing.

Jari's comments about well fitted species versus poorly fitted species
and only plotting the well-fitted ones a la recent Canoco/Canodraw could
be done, but again we need to know about what you want to do.
See ?goodness.cca for some of these definitions.

> 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, 

If you want to know which species a good indicators of site type, then I
think you might want to look at a classification tree to predict the
site type factor. But then to make these things work you'd need a lot of
samples.

If you need further help, perhaps you can explain what it is you want to
do?

HTH

G

> Amelie D’Astous
> Laval university
> Quebec
> 

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