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

Jari,

I am here again ... :)
So, to try having a comparison of the real goodness of my metaMDS data I
tried to perform a DCA (with same input table)
Then please forgive me if I do somethign wrong with it... That's my R code:

Why DCA? What lead you to torture your data so?

decorana(sqrtABCD, iweigh=0, ira=0) -> DCA.1
DCA.1

Call:
decorana(veg = sqrtABCD, iweigh = 0, ira = 0)

Detrended correspondence analysis with 26 segments.
Rescaling of axes with 4 iterations.

                  DCA1   DCA2   DCA3   DCA4
Eigenvalues     0.6688 0.5387 0.4822 0.3752
Decorana values 0.7912 0.5795 0.4145 0.2931
Axis lengths    5.9974 3.7036 3.6121 3.3802

In that situation the graph is still good but the differences between the
two clades are little more confused, maybe in the axe II (I mean the
vertical one) in this case there is a better separation.
What do the "Decorana values" really mean?

?decorana

Basically, in the original DECORANA code the Eigenvalues reported were
computed at the wrong stage of the "detrending" processes. Jari realised
this when interfacing the old DECORANA code with R. Jari altered the
code to compute the correct Eigenvalues, but chose to also report the
values you'd get from DECORANA or Canoco to stop people complaining that
vegan was doing DCA incorrectly.

 And how about the segments?

What about them? Do you know how DCA works? The standard detrending
breaks the first (D)CA axis into 26 sequential chunks or segements. the
26 is the default, but it can be changed. Within each chunk, the mean
trial site score for axis 2 for sites in that chunk is subtracted from
the trial axis 2 site scores of the sites in the chunk. This detrending
is what gets rid of the "arch" found in some CA plots and is the reason
DCA was invented.

How can I do something better?

Are you trying to separate the two clades? Do you know a priori which
samples belong to which clade? If so, one of the many classification
methods in R would be more useful as they look to separate the a priori
defined groups best. The methods you have been using thus far aim to
represent the dissimilarities between samples best in a low dimensional
space.

HTH

G

Many thank you in advance,

G.

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