Re: [R-sig-eco] Measurement distance for proportion data
I would also suggest to give a try to the Aitchison distance. To do so, you can use the âcompositionsâ 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 : âmardiâ, â13â âmaiâ â2014 â11â:â21 à : Zbigniew Ziembik Cc : 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]] >> >> ___ >> R-sig-ecology mailing list >> R-sig-ecology@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > ___ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Measurement distance for proportion data
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]] >> >> ___ >> R-sig-ecology mailing list >> R-sig-ecology@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > ___ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Measurement distance for proportion data
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]] >> >> ___ >> R-sig-ecology mailing list >> R-sig-ecology@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > ___ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Measurement distance for proportion data
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 ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Measurement distance for proportion data
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]] > > ___ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology