how to calculate "axis variance" in metaMDS, pakage vegan?

Dear disputants.

If this is a poll, I'd like to second Gabriel's email, with respect to 
his comments on the usefulness of the R-sig-ecology mailing list AND his 
comments on (n)MDS + adonis AND his point on slowly developing the 
standard of your research field, rather than immediately dwarfing 
publication chances by using more "appropriate" but revolutionising 
methods in the field.
My own experiences are that 1. reviewers become ever more statistically 
literate (so I guess one should have no problems in moving from a PCA to 
a PCoA/MDS); and 2. ecological patterns are relatively robust to the 
ordination method (so not much is lost by using more, well, traditional 
approaches).

Cheers,

Carsten

gabriel singer wrote:

Hi Gian and others,

I think we better stop worrying about subjective interpretations of 
emotional backgrounds of what in other aspects are absolutely helpful 
discussion threads... I guess part of the challenge on this mailing 
list is to span the whole range of expertise with useful 
discussion/output/help for everyone, be it a student or an expert. I 
found this mailing list very helpful many times for my own questions, 
but also very informative when just following the threads on other 
questions...

Gian, in my opinion, 2 dimensions are absolutely ok, especially if 
they do visualize an (obvious) effect in your study. In other words, 
if 2 dimensions show you an effect of "Host" but not of "Area", the 
effect is obviously strong enough. Then I would not worry about stress 
too much. However, there may still be an effect of "Area", maybe 
visible in more dimensions, but it?s obviously of minor importance.

I personally like a combination of NMDS with the permutational MANOVA 
approach (by Marti Anderson) implemented in the function adonis() in 
vegan. You can use the same dissimilarity measure (Bray-Curtis) used 
for the NMDS and can test the "Area" vs. the "Host" effect on parasite 
(was it?) composition. I think that could be a very useful complement 
to an NMDS-derived ordination plot and then you may also regard 
high-stress "representations" (and that?s what all the low-dimensional 
ordination plots really ARE!) in a different light.

Complementations like the permanova are in my opinion better than 
trying the full spectrum of ordination methods until finally some kind 
of pattern gets uncovered (comes quite close to the much too often 
encountered data-fishing expeditions....). And though copying analysis 
strategies is probably not quite like throwing yourself in front of a 
bus, there is some benefit in using what people working in a specific 
field regard their "standard" methods (wait for the reviews to 
discover this). In any case, a responsible choice for a type of 
analysis is oriented along the study design and the data at hand.

cheers, gabriel