Re: [R-sig-eco] Measurement distance for proportion data

2014-05-13 Thread Zbigniew Ziembik
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 +, 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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Re: [R-sig-eco] Measurement distance for proportion data

2014-05-13 Thread Rich Shepard

On Tue, 13 May 2014, Zbigniew Ziembik wrote:


or (commercial):
Aitchison, J. 2003. The Statistical Analysis of Compositional Data. The
Blackburn Press.


  There's also: Analyzing Compositional Data with R by van den Boogaart, K.
Gerald,Tolosana-Delgado, Raimon. Published by Springer in their UseR!
series.

Rich

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Re: [R-sig-eco] Measurement distance for proportion data

2014-05-13 Thread Jari Oksanen
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 +, 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 
   
  [[alternative HTML version deleted]]
 
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Re: [R-sig-eco] Measurement distance for proportion data

2014-05-13 Thread Jari Oksanen
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 +, 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 
   
  [[alternative HTML version deleted]]
 
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Re: [R-sig-eco] Measurement distance for proportion data

2014-05-13 Thread separent
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 +, 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
  [[alternative HTML version deleted]]
 
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