I would also suggest to give a try to the Aitchison distance. To do so, you can use the “compositions” package. You transform the proportions to centered log-ratios or isometric log-ratios (clr and ilr functions, respectively), then compute the Euclidean distance through transformed data - both transformations should return the same distances.


library(compositions)
library(vegan)
data(AnimalVegetation)
region = factor(ifelse(AnimalVegetation[,5]==1, "A", "B")) # region label
comp = acomp(AnimalVegetation[,1:4]) # proportions closed between 0 and 1
# comp[region=="A",] = acomp(comp[region=="A",]) + c(1,1,2,1) # perturbation on region A for testing purposes
bal = ilr(comp) # isometric log-ratios

dist = vegdist(bal, method="euclidean") # Aitchison dissimilarity matrix
mod = betadisper(dist, region)
mod
plot(mod)
adonis(dist ~ region)


Cheers,


Essi Parent






De : Jari Oksanen
Envoyé : ‎mardi‎, ‎13‎ ‎mai‎ ‎2014 ‎11‎:‎21
À : Zbigniew Ziembik
Cc : <r-sig-ecology@r-project.org>





Typical dissimilarity indices are of form difference/adjustment, where the adjustment takes care of forcing the index to the range 0..1, and handles varying total abundances / richnesses. If you have proportional data, you may not need the adjustment at all, but you can just use any index. That is, it does not matter so awfully much what index you use, and for many practical purposes it does not matter if data are proportional. Actually, several indices may be equal to each with with proportional data. For instance, Manhattan, Bray-Curtis and Kulczynski indices are all identical. All you need to decide is which name you use for your index -- numbers do not change.

The analysis of proportional data usually covers very different classes of models than ANOSIM and friends. Dissimilarities are not usually involved in these models. One aspect in proportional data is that only M-1 of M variables really are independent. However, this really needs to be taken into account if M is low. I have no idea how is that in your case. 

Cheers, Jari Oksanen
On 13/05/2014, at 15:32 PM, Zbigniew Ziembik wrote:

> I am not sure, but it seems that your problem is related to
> compositional data analysis. You can probably use Aitchison distance to
> estimate separation between proportions.
> Take a (free) look at:
> http://www.leg.ufpr.br/lib/exe/fetch.php/pessoais:abtmartins:a_concise_guide_to_compositional_data_analysis.pdf.
> http://dugi-doc.udg.edu/bitstream/10256/297/1/CoDa-book.pdf.
> 
> or (commercial):
> Aitchison, J. 2003. The Statistical Analysis of Compositional Data. The
> Blackburn Press.
> 
> Best regards,
> ZZ
> 
> 
> Dnia 2014-05-12, pon o godzinie 16:37 +0000, Javier Lenzi pisze:
>> Dear all, 
>> I'm doing data exploration on seabirds trophic ecology data and I am using ANOSIM to evaluate possible differences in diet during breeding and non-breeding seasons. As starting point I am using some classical indexes such as %FO (relative frequency of occurrence), N (number of prey counted in the pooled sample of pellets), %N (N as a percentage of the total number of prey of all food types in the pooled sample), V (total volume of all prey in the pooled sample), and IRI (index of relative importance). 
>> I have a concern on which similarity meassurement should I use in ANOSIM for those indexes that are proportions.. I am aware that for instance Bray-Curtis is used for count data (e.g. N) and Jaccard is used for presence-absence data (which I don't have), however I did not find a proper distance measurement for proportion data. Please, could you help me to find a proper distance measurement for these proportion data? 
>> Thank you very much in advance. Regards,Javier Lenzi            
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>> 
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