Thank you all for your ideas. I'll probably explore further Liam's method. sincerely,
john On Tue, Mar 12, 2013 at 5:33 PM, Liam J. Revell <[email protected]> wrote: > I did a little further exploration of this proposed "method" - the results & > discussion are here: > http://blog.phytools.org/2013/03/investigating-whether-rate-of-one.html > > Maybe this will be of some help in deciding the best approach to go forward > with. > > > All the best, Liam > > Liam J. Revell, Assistant Professor of Biology > University of Massachusetts Boston > web: http://faculty.umb.edu/liam.revell/ > email: [email protected] > blog: http://blog.phytools.org > > On 3/11/2013 6:03 PM, Liam J. Revell wrote: >> >> Hi John & Matt. >> >> What about the admittedly ad hoc approach of computing the correlation >> between the states at ancestral nodes for x & the squared contrasts for >> corresponding nodes for y? Then you can generate a null distribution for >> the test statistic (say, a Pearson or Spearman rank correlation) by >> simulation. This seems to give reasonable type I error when the null is >> correct, and when I simulate under the alternative (i.e., the rate of >> Brownian evolution along a branch depends on the state at the >> originating node) it sometimes is significant. >> >> Here's a function that does what I've described (I think - please check >> it carefully!). It needs phytools and all dependencies. >> >> ratebystate<-function(tree,x,y,nsim=100,method=c("pearson","spearman")){ >> method<-method[1] >> if(!is.binary.tree(tree)) tree<-multi2di(tree) >> V<-phyl.vcv(cbind(x,y),vcv(tree),lambda=1)$R >> a<-fastAnc(tree,x) >> b<-pic(y,tree)[names(a)]^2 >> r<-cor(a,b,method=method) >> beta<-setNames(lm(b~a)$coefficients[2],NULL) >> foo<-function(tree,V){ >> XY<-sim.corrs(tree,V) >> a<-fastAnc(tree,XY[,1]) >> b<-pic(XY[,2],tree)[names(a)]^2 >> r<-cor(a,b,method=method) >> return(r) >> } >> r.null<-c(r,replicate(nsim-1,foo(tree,V))) >> P<-mean(abs(r.null)>=abs(r)) >> return(list(beta=beta,r=r,P=P,method=method)) >> } >> >> Perhaps this is a good idea. I don't know. All the best, Liam >> >> Liam J. Revell, Assistant Professor of Biology >> University of Massachusetts Boston >> web: http://faculty.umb.edu/liam.revell/ >> email: [email protected] >> blog: http://blog.phytools.org >> >> On 3/11/2013 4:03 PM, Matt Pennell wrote: >>> >>> John, >>> >>> This is a tricky question. If your independent variables were >>> discrete, you >>> could use a stochastic character mapping approach to map "state regimes" >>> onto your tree and ask whether the regimes had different rates using a >>> model selection approach. (This could be done with the R packages >>> phytools >>> or ouwie, depending on what models of trait evolution you are >>> interested in >>> investigating). >>> >>> However, since your independent variables are continuous, there is no >>> equivalent of the stochastic mapping approach to answer this question. As >>> far as I am aware, no model-based framework exists to address your >>> question >>> (sorry that to be a downer). One could conceivably derive such a model >>> following Rich Fitzjohn's approach in QuaSSE (Sys Bio 2010) but >>> instead of >>> the rate of speciation/extinction depending on the state of the >>> continuous >>> variable, let the rate of a second variable be a function of the state of >>> the first. But this would certainly be a lot of effort to accomplish. >>> >>> I agree with you as I do not think getting rates from standardized >>> independent contrasts (sensu Garland 1992) will really allow you to >>> get at >>> your question. >>> >>> the TL;DR version is that no such method exists (at least to my >>> knowledge) >>> but this would definitely be a useful innovation. >>> >>> hope this was at least somewhat helpful. >>> >>> cheers, >>> matt >>> >>> >>> >>> >>> On Mon, Mar 11, 2013 at 12:50 PM, john d <[email protected]> wrote: >>> >>>> Dear colleagues, >>>> >>>> I got a philosophical/methodological/practical question. >>>> >>>> I have a continuous dependent variable (e.g. range size) and a few >>>> "independent" variables (e.g. body mass, encephalization ratio), and I >>>> want to test how the rate of evolution of the dependent variable is >>>> affected by the independent variables. The PCMs that I'm familiar with >>>> cannot be used to answer this question, because they usually try to >>>> predict the dependent variable based on the independent variables >>>> (e.g. PGLM) instead of looking at the rates of evolution. The whole >>>> thing gets tricky if one decides to deal with the rates of evolution >>>> of the indepentent variables as well (or not). >>>> >>>> I guess one possibility would be to use standardized independent >>>> contrasts (as in Garland 1992) for the estimation of rates. But I'm >>>> not sure how to try to predict the *rate* of evolution of range size >>>> from the values of the "independent" variables (and not their own >>>> rates, which is what I guess I'd get if I transformed all variables >>>> into standardized contrasts). >>>> >>>> Any thoughts? >>>> >>>> John >>>> >>>> _______________________________________________ >>>> R-sig-phylo mailing list - [email protected] >>>> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo >>>> Searchable archive at >>>> http://www.mail-archive.com/[email protected]/ >>>> >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> R-sig-phylo mailing list - [email protected] >>> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo >>> Searchable archive at >>> http://www.mail-archive.com/[email protected]/ >>> >> >> _______________________________________________ >> R-sig-phylo mailing list - [email protected] >> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo >> Searchable archive at >> http://www.mail-archive.com/[email protected]/ >> > _______________________________________________ R-sig-phylo mailing list - [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/[email protected]/
