Hi all again,

And thank you all for the kind responses...

I do not want to torture myself for sure... :) I red (lot of) publications
about fungal community ecology studies (soil fungi), my research field
indeed, and all uses NMDS or DCA as ordination techniques... So, I am only
trying to do my best useing R for calculating them...

What I need now is a good environmental interpretation of my work...

Then I found the fantastic Jari's pdf about "Multivariate Analysis of
Ecological Communities in R: vegan tutorial" and I went to the passage about
factors and vectors fitting...
That's my R code:

> NMS.ABCDsqrt

Call:
metaMDS(comm = sqrtABCD, distance = "bray", k = 4, trymax = 100,
autotransform = F)

Nonmetric Multidimensional Scaling using isoMDS (MASS package)

Data:     sqrtABCD
Distance: bray shortest

Dimensions: 4
Stress:     11.68632
Two convergent solutions found after 2 tries
Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on ‘sqrtABCD’

> envfit(NMS.ABCDsqrt, env.table, permu=1000) ->NMS.ABCDsqrtef
> NMS.ABCDsqrtef

***FACTORS:

Centroids:
              NMDS1   NMDS2   NMDS3   NMDS4
CommunityA  -0.3821  0.3822 -0.1173 -0.1232
CommunityB  -0.1849  0.2748  0.0076 -0.0720
CommunityC   0.2206 -0.4261 -0.0505  0.1197
CommunityD   0.3465 -0.2310  0.1603  0.0756
HostCorylus -0.2835  0.3285 -0.0549 -0.0976
HostOstrya   0.2835 -0.3285  0.0549  0.0976

Goodness of fit:
              r2   Pr(>r)
Community 0.2009 0.001998 **
Host      0.1818 0.000999 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 1000 permutations.

> names(NMS.ABCDsqrtef)
[1] "vectors" "factors"
> NMS.ABCDsqrtef$vectors
NULL
>

I have an enviromental matrix (env.table) that contains only area (A,B,C,D)
and tree species (Corylus sp. and Ostrya sp. ) differentiation, so I have
the area and the tree species of each samples is related to...
I could plot factors on the graph but not vectors because there aren't
vectors in reason to the absence of numerical data in the env. matrix...
isn't it right? Aren't the R2 values too low?

Many many thank you for answering...

Gian










> 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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