[R-sig-eco] Fwd: how to calculate "axis variance" in metaMDS, pakage vegan?
gabriel singer
gabriel.singer at univie.ac.at
Wed Dec 9 10:25:08 CET 2009
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
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]]
>
>
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--
Dr. Gabriel Singer
Department of Freshwater Ecology - University of Vienna
and Wassercluster Lunz Biologische Station GmbH
+43-(0)664-1266747
gabriel.singer at univie.ac.at
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