[R-sig-eco] Fwd: how to calculate "axis variance" in metaMDS, pakage vegan?
Gavin Simpson
gavin.simpson at ucl.ac.uk
Wed Dec 9 10:36:57 CET 2009
On Wed, 2009-12-09 at 10:25 +0100, gabriel singer wrote:
> Gian,
>
> You may also want to use betadisper() to check whether the host effect
> is due to differences in "location" or "dispersion" (or both). This is
> equivalent to checking homogeneity of variance when running a classical
> ANOVA.
>
> cheers, g
Good point Gabriel, but I'd caution against using betadisper just at the
moment in Vegan. A user, and subsequently confirmed by Jari, notified us
that the default (and currently only) method using the dispersion around
the group centroid (average) and a permutation test was
anti-conservative. Since then Jari has written code to allow us to
include the dispersion around the spatial median within betadisper and
initial tests suggests this has the right Type I error rate in the
permutation test. I had hoped to have included this by now, but having
been under the weather for the past month I have not yet finished
working on it.
An updated version should be on r-forge in the next few days.
G
>
>
> Gian Maria Niccolò Benucci wrote:
> > Jari, Gavin, Chris, Gabriel and Carsten...
> >
> > Many thank you all for your support and kindness... and for your competence
> > and experience that could not be ever comparized to mine at least in that
> > stuffs...
> >
> > Gabriel said: .*..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...
> > *
> > I complitely agree about that, so here I am to go deeper inside my
> > statistical problems...
> >
> > As Gavin argued the plot:
> >
> >
> >> NMS.2$stress
> >>
> > [1] 24.53723
> >
> >> NMS.3$stress
> >>
> > [1] 16.29226
> >
> >> NMS.4$stress
> >>
> > [1] 11.79951
> >
> >> plot(2:4, c(24.53723, 16.29226, 11.79951), type = "b")
> >>
> >
> > didn't show significally differences...
> >
> > ...so as him suggested I did the stressplot() and got shepard graphs...
> > (just to specify, sqrtABCD is the square roots transforming of the species
> > matrix)
> >
> >
> >> stressplot(NMS.2)
> >>
> > Using step-across dissimilarities:
> > Too long or NA distances: 230 out of 780 (29.5%)
> > Stepping across 780 dissimilarities...
> >
> >
> > Non-metric fit, R2=0.94
> > Linear fit, R2=0.719
> >
> >
> >> stressplot(NMS.3)
> >>
> > Using step-across dissimilarities:
> > Too long or NA distances: 230 out of 780 (29.5%)
> > Stepping across 780 dissimilarities...
> >
> >
> > Non-metric fit, R2=0.973
> > Linear fit, R2=0.815
> >
> >
> >> stressplot(NMS.4)
> >>
> > Using step-across dissimilarities:
> > Too long or NA distances: 230 out of 780 (29.5%)
> > Stepping across 780 dissimilarities...
> >
> > Non-metric fit, R2=0.986
> > Linear fit, R2=0.875
> >
> > >From this data is clear that the fit is better for the NMS.4 (k=4) also the
> > blue points into the graph are more near to red line, less spare around the
> > graph space...
> >
> > But maybe the R2 values of the NMS.2 aren't so bad in correlation terms, are
> > they?
> >
> > In reason of what Gabriel said: *...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.*..
> >
> >
> >
> >> adonis(sqrtABCD ~ Host*Community, method="bray", data=env.table,
> >>
> > permutations=99)
> >
> > Call:
> > adonis(formula = sqrtABCD ~ Host * Community, data = env.table,
> > permutations = 99, method = "bray")
> >
> > Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
> > Host 1.00000 1.64429 1.64429 5.47874 0.1242 0.01 **
> > Community 2.00000 0.78834 0.39417 1.31337 0.0596 0.23
> > Residuals 36.00000 10.80441 0.30012 0.8162
> > Total 39.00000 13.23705 1.0000
> > ---
> > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >
> >
> > ...So, I would explain a little about my datasets:
> >
> > - the species matrix is done by roots samples in which were counted the
> > ectomycorrhizal fungal species present (cells entities are different tips
> > individuals);
> > - sample where taken into four "Area" (A,B,C,D). The ares are about 30
> > meters far away one to each other;
> > - areas A and B are both form Corylus roots while areas C and D are both
> > from Ostrya roots.
> >
> > To be more clear that is the enviromental matix used:
> >
> >
> >> env.table
> >>
> > Community Host
> > A1 A Corylus
> > A2 A Corylus
> > A3 A Corylus
> > A4 A Corylus
> > A5 A Corylus
> > A6 A Corylus
> > A7 A Corylus
> > A8 A Corylus
> > A9 A Corylus
> > A10 A Corylus
> > B1 B Corylus
> > B2 B Corylus
> > B3 B Corylus
> > B4 B Corylus
> > B5 B Corylus
> > B6 B Corylus
> > B7 B Corylus
> > B8 B Corylus
> > B9 B Corylus
> > B10 B Corylus
> > C1 C Ostrya
> > C2 C Ostrya
> > C3 C Ostrya
> > C4 C Ostrya
> > C5 C Ostrya
> > C6 C Ostrya
> > C7 C Ostrya
> > C8 C Ostrya
> > C9 C Ostrya
> > C10 C Ostrya
> > D1 D Ostrya
> > D2 D Ostrya
> > D3 D Ostrya
> > D4 D Ostrya
> > D5 D Ostrya
> > D6 D Ostrya
> > D7 D Ostrya
> > D8 D Ostrya
> > D9 D Ostrya
> > D10 D Ostrya
> >
> >
> > ...maybe could be helpfull to say that I calculated diversity indices
> > (richness, shannon, simpson and evenness) for my 4 areas and I use ANOVA to
> > see if them are diffent one from each other.
> > The results show me that area A and B are always different form areas C and
> > D but no differences are between them, so clearly Corylus fungal community
> > is alwasy different from Ostrya one.
> >
> > ...So, I think that "Host" effect is clear while the effect of "Community"
> > couldn't be the same in reason to that areas are similar 2 by 2, ...is it
> > right?
> >
> > When I plot the MNS.2 and I watch to the Graph I clearly see that sample
> > points of A,B areas or Corylus are positioned on the left side while areas C
> > and D of Ostrya are more sparse and are positioned into the low right
> > side...
> >
> > So, what else to say... I'll leave you space for any comments :))))
> >
> > Tank you all,
> >
> > Gian
> >
> > [[alternative HTML version deleted]]
> >
> >
> > ------------------------------------------------------------------------
> >
> > _______________________________________________
> > R-sig-ecology mailing list
> > R-sig-ecology at r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> >
>
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Dr. Gavin Simpson [t] +44 (0)20 7679 0522
ECRC, UCL Geography, [f] +44 (0)20 7679 0565
Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk
Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/
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