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Today's Topics:
1. Re: Measurement distance for proportion data (Zbigniew Ziembik)
2. Re: Measurement distance for proportion data (Rich Shepard)
3. Re: Measurement distance for proportion data (Jari Oksanen)
4. Re: Measurement distance for proportion data (Jari Oksanen)
5. Re: Measurement distance for proportion data (separent at yahoo.com)
----------------------------------------------------------------------
Message: 1
Date: Tue, 13 May 2014 14:32:25 +0200
From: Zbigniew Ziembik <ziembik at uni.opole.pl>
To: r-sig-ecology at r-project.org
Subject: Re: [R-sig-eco] Measurement distance for proportion data
Message-ID: <1399984345.4938.16.camel at vista>
Content-Type: text/plain; charset="UTF-8"
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
[[alternative HTML version deleted]]
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Message: 2
Date: Tue, 13 May 2014 07:56:24 -0700 (PDT)
From: Rich Shepard <rshepard at appl-ecosys.com>
To: r-sig-ecology at r-project.org
Subject: Re: [R-sig-eco] Measurement distance for proportion data
Message-ID: <alpine.LNX.2.11.1405130754240.11126 at localhost>
Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
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
------------------------------
Message: 3
Date: Tue, 13 May 2014 15:20:40 +0000
From: Jari Oksanen <jari.oksanen at oulu.fi>
To: Zbigniew Ziembik <ziembik at uni.opole.pl>
Cc: "<r-sig-ecology at r-project.org>" <r-sig-ecology at r-project.org>
Subject: Re: [R-sig-eco] Measurement distance for proportion data
Message-ID: <966051C3-1D52-4503-B789-279C705B6871 at oulu.fi>
Content-Type: text/plain; charset="us-ascii"
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
[[alternative HTML version deleted]]
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------------------------------
Message: 4
Date: Tue, 13 May 2014 15:21:10 +0000
From: Jari Oksanen <jari.oksanen at oulu.fi>
To: Zbigniew Ziembik <ziembik at uni.opole.pl>
Cc: "<r-sig-ecology at r-project.org>" <r-sig-ecology at r-project.org>
Subject: Re: [R-sig-eco] Measurement distance for proportion data
Message-ID: <811C9A6E-3D7B-41D3-8FC9-03D375408A40 at oulu.fi>
Content-Type: text/plain; charset="us-ascii"
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
[[alternative HTML version deleted]]
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------------------------------
Message: 5
Date: Tue, 13 May 2014 17:28:21 +0000
From: <separent at yahoo.com>
To: Jari Oksanen <jari.oksanen at oulu.fi>, Zbigniew Ziembik
<ziembik at uni.opole.pl>
Cc: "=?utf-8?Q?<r-sig-ecology at r-project.org>?="
<r-sig-ecology at r-project.org>
Subject: Re: [R-sig-eco] Measurement distance for proportion data
Message-ID: <141017.79987.bm at smtp211.mail.bf1.yahoo.com>
Content-Type: text/plain
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
Envoy?? : ???mardi???, ???13??? ???mai??? ???2014 ???11???:???21
?? : Zbigniew Ziembik
Cc : <r-sig-ecology at 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
[[alternative HTML version deleted]]
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