Hi Nina and everyone, One thing to consider is that not all zero data are the same. Zeros under a model of continuous trait evolution with a gaussian process as assumed under Brownian motion and OU processes would occasionally cross zero, maybe go negative, etc. For example if you were modeling something like the deviation from average height. You may have a lot of individuals that are at zero because they are all average, but their offspring will quickly move off zero as some will be taller, some shorter than average.
On the other hand, many of us measure traits which disappear, for example scale counts on fish. Numbers of scale rows vary while there are scales, but once they disappear, those lineages will be at zero, perhaps for a very long time. In this case it is no longer behaving as a gaussian process with small changes expected every time period. We usually think of these more as a "threshold trait". Maybe there is some hormone or something else (a hidden variable) underlying the determination of scales or no scales, and once it goes below threshhold the scales disappear. The hidden variable may fit model assumptions, but not the scale counts (what we can see and measure). For example, the scale counts can never go negative. With a value like height, it also never goes negative, but usually we are far away from that zero boundary so we can casually ignore that problem:). Or we can do a log-transform, or something else that transforms the data onto a different scale. Anyway, the a! bsorbing boundary zeros are, I think, an example of what Ted is talking about. So it depends on the nature of your data. On the other hand, for the other variable, if there is just not much variation, but it's not stuck on any particular value (doesn't appear to have any absorbing boundary), I think that's less of a problem. HTH, Marguerite On Apr 25, 2012, at 7:17 AM, Theodore Garland Jr wrote: > Read over the Blomberg et al. (2003) paper. > K is intended for continuous-valued traits and/or those evolving similar to > Brownian motion. > You could report it if you wished, but I would add that caveat if you do. > > The randomization test should be robust in any case. > > Cheers, > Ted > > > From: Nina Hobbhahn [n.hobbh...@gmail.com] > Sent: Wednesday, April 25, 2012 9:19 AM > To: Theodore Garland Jr > Cc: Alejandro Gonzalez; Hunt, Gene; Enrico Rezende; r-sig-phylo@r-project.org > Subject: Re: [R-sig-phylo] Normality requirement for assessment of lambda > with phylosig (phytools) and fitContinuous (geiger) > > Thanks all for your helpful contributions! I will use phylosignal. > > Ted, I'm not sure I understand your last comment, "when the data are not > though of as continuous-valued and/or evolving similar to Brownian motion". > What do you mean by that? Also, are you suggesting that I report the > presence/absence of phylogenetic signal, but not the value of the K statistic? > > Many thanks again, > > Nina > > > On 2012-04-25, at 5:54 PM, Theodore Garland Jr wrote: > >> However, calculating a K statistic is strange when the data are not thought >> of as continuous-valued and/or evolving similar to Brownian motion. The >> randomization test is OK, however. >> >> Cheers, >> Ted >> >> From: Alejandro Gonzalez [alejandro.gonza...@ebd.csic.es] >> >> Sent: Wednesday, April 25, 2012 8:46 AM >> >> To: Theodore Garland Jr >> >> Cc: Nina Hobbhahn; r-sig-phylo@r-project.org >> >> Subject: Re: [R-sig-phylo] Normality requirement for assessment of lambda >> with phylosig (phytools) and fitContinuous (geiger) >> >> >> >> >> >> >> >> Hello, >> >> >> >> >> >> The library picante in R implements Blomberg et al (2003) K estimate, Liam's >> phytools package does as well. Phytools has the added advantage, if I >> remember correctly, of allowing users to estimate K including within species >> variation. >> >> >> >> >> >> Cheers >> >> >> >> >> >> Alejandro >> >> >> >> >> >> >> >> >> >> >> On 25, Apr 2012, at 5:29 PM, Theodore Garland Jr wrote: >> >> >> >> I would suggest the randomization test in Blomberg et al. (2003). This will >> give a valid significance test of the null hypothesis of no phylogenetic >> signal. By itself, it does not give a measure of the strength (or amount) >> of phylogenetic signal. Not >> sure if it is implented in r. If not, I can send our Matlab code. >> >> >> >> Cheers, >> >> Ted >> >> >> >> Theodore Garland, Jr. >> >> Professor >> >> Department of Biology >> >> University of California, Riverside >> >> Riverside, CA 92521 >> >> Office Phone: (951) 827-3524 >> >> Home Phone: (951) 328-0820 >> >> Facsimile: (951) 827-4286 = Dept. office (not confidential) >> >> Email: tgarl...@ucr.edu >> >> http://www.biology.ucr.edu/people/faculty/Garland.html >> >> >> >> Experimental Evolution: Concepts, Methods, and Applications of Selection >> Experiments. 2009. >> >> Edited by Theodore Garland, Jr. and Michael R. Rose >> >> http://www.ucpress.edu/book.php?isbn=9780520261808 >> >> (PDFs of chapters are available from me or from the individual authors) >> >> >> >> ________________________________________ >> >> From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org] >> on behalf of Nina Hobbhahn [n.hobbh...@gmail.com] >> >> Sent: Wednesday, April 25, 2012 1:55 AM >> >> To: r-sig-phylo@r-project.org >> >> Subject: [R-sig-phylo] Normality requirement for assessment of lambda with >> phylosig (phytools) and fitContinuous (geiger) >> >> >> >> Dear fellow list users, >> >> >> >> I would like to assess the magnitude of phylogenetic signal in two sets of >> continuous data. Set 1 contains numerous zeros and is therefore non-normal. >> Set 2 contains very little variation and is non-normal due to >> underdispersion. Given that both data sets are >> largely immune to transformations to normality, I am wondering whether the >> lambda estimates for untransformed data derived from phylosig and >> fitContinuous will be meaningful? If not, can you recommend transformations >> or other methods of phylogenetic-signal >> assessment that would be preferable? >> >> >> >> Thank you very much, >> >> >> >> Nina >> >> >> >> >> >> >> >> Dr. Nina Hobbhahn >> >> Post-doctoral fellow >> >> Lab of Prof. S. D. Johnson >> >> School of Life Sciences >> >> University of KwaZulu-Natal >> >> Private Bag X01 >> >> Scottsville, Pietermaritzburg, 3201 >> >> South Africa > > _______________________________________________ > R-sig-phylo mailing list > R-sig-phylo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-phylo ____________________________________________ Marguerite A. Butler Associate Professor Department of Biology University of Hawaii 2450 Campus Rd., Dean Hall Rm. 2 Honolulu, HI 96822 Office: 808-956-4713 Dept: 808-956-8617 Lab: 808-956-5867 FAX: 808-956-9812 http://www.hawaii.edu/zoology/faculty/butler.html http://www2.hawaii.edu/~mbutler http://www.hawaii.edu/zoology/ [[alternative HTML version deleted]] _______________________________________________ R-sig-phylo mailing list R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
