Dear all, Thank you very much for your help, I really appreciate it.
Thanks, Best regards, Javier. > From: [email protected] > Subject: R-sig-ecology Digest, Vol 74, Issue 6 > To: [email protected] > Date: Wed, 14 May 2014 12:00:01 +0200 > > Send R-sig-ecology mailing list submissions to > [email protected] > > To subscribe or unsubscribe via the World Wide Web, visit > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > or, via email, send a message with subject or body 'help' to > [email protected] > > You can reach the person managing the list at > [email protected] > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of R-sig-ecology digest..." > > > 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 ([email protected]) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Tue, 13 May 2014 14:32:25 +0200 > From: Zbigniew Ziembik <[email protected]> > To: [email protected] > Subject: Re: [R-sig-eco] Measurement distance for proportion data > Message-ID: <1399984345.4938.16.camel@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]] >> >> _______________________________________________ >> R-sig-ecology mailing list >> [email protected] >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > > > ------------------------------ > > Message: 2 > Date: Tue, 13 May 2014 07:56:24 -0700 (PDT) > From: Rich Shepard <[email protected]> > To: [email protected] > Subject: Re: [R-sig-eco] Measurement distance for proportion data > Message-ID: <alpine.LNX.2.11.1405130754240.11126@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 <[email protected]> > To: Zbigniew Ziembik <[email protected]> > Cc: "<[email protected]>" <[email protected]> > Subject: Re: [R-sig-eco] Measurement distance for proportion data > Message-ID: <[email protected]> > 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]] >>> >>> _______________________________________________ >>> R-sig-ecology mailing list >>> [email protected] >>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> >> _______________________________________________ >> R-sig-ecology mailing list >> [email protected] >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > > > ------------------------------ > > Message: 4 > Date: Tue, 13 May 2014 15:21:10 +0000 > From: Jari Oksanen <[email protected]> > To: Zbigniew Ziembik <[email protected]> > Cc: "<[email protected]>" <[email protected]> > Subject: Re: [R-sig-eco] Measurement distance for proportion data > Message-ID: <[email protected]> > 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]] >>> >>> _______________________________________________ >>> R-sig-ecology mailing list >>> [email protected] >>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> >> _______________________________________________ >> R-sig-ecology mailing list >> [email protected] >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > > > ------------------------------ > > Message: 5 > Date: Tue, 13 May 2014 17:28:21 +0000 > From: <[email protected]> > To: Jari Oksanen <[email protected]>, Zbigniew Ziembik > <[email protected]> > Cc: "=?utf-8?Q?<[email protected]>?=" > <[email protected]> > Subject: Re: [R-sig-eco] Measurement distance for proportion data > Message-ID: <[email protected]> > 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 > Envoyé : ‎mardi‎, ‎13‎ ‎mai‎ ‎2014 ‎11‎:‎21 > À : Zbigniew Ziembik > Cc : <[email protected]> > > > > > > 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]] >>> >>> _______________________________________________ >>> R-sig-ecology mailing list >>> [email protected] >>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> >> _______________________________________________ >> R-sig-ecology mailing list >> [email protected] >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > _______________________________________________ > R-sig-ecology mailing list > [email protected] > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > [[alternative HTML version deleted]] > > > ------------------------------ > > _______________________________________________ > R-sig-ecology mailing list > [email protected] > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > > End of R-sig-ecology Digest, Vol 74, Issue 6 > ******************************************** _______________________________________________ R-sig-ecology mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
