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 <dobzhan...@gmail.com> 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 - R-sig-phylo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-phylo > Searchable archive at > http://www.mail-archive.com/r-sig-phylo@r-project.org/ > [[alternative HTML version deleted]] _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/
