This is a good point and one that is often glossed over. We talked about it quite a bit here:
Garland, T., Jr., A. W. Dickerman, C. M. Janis, and J. A. Jones. 1993. Phylogenetic analysis of covariance by computer simulation. Systematic Biology 42:265–292. http://www.biology.ucr.edu/people/faculty/Garland/GarlEA93.pdf Surely you want to do various descriptive statistics on your simulated data sets to see how they compare with the real one, and presumably you want some of those to include the phylogenetic versions (e.g., conventional and phylogenetic estimates of the correlation coefficient if you are simulating two traits). I think it is also really important to consider models that have limits to trait evolution (again, see the paper listed above). Those limits can interact strongly with starting (root) values, especially if you include evolutionary trends. Check Figure 1 in this paper: Diaz-Uriarte, R., and T. Garland. 1996. Testing hypotheses of correlated evolution using phylogenetically independent contrasts: sensitivity to deviations from Brownian motion. Systematic Biology 45:27–47. http://www.biology.ucr.edu/people/faculty/Garland/DiazGa96.pdf Cheers, Ted Theodore Garland, Jr., Professor Department of Biology University of California, Riverside Riverside, CA 92521 Office Phone: (951) 827-3524 Facsimile: (951) 827-4286 (not confidential) Email: tgarl...@ucr.edu http://www.biology.ucr.edu/people/faculty/Garland.html http://scholar.google.com/citations?hl=en&user=iSSbrhwAAAAJ Director, UCR Institute for the Development of Educational Applications Editor in Chief, Physiological and Biochemical Zoology Fail Lab: Episode One http://testtube.com/faillab/zoochosis-episode-one-evolution http://www.youtube.com/watch?v=c0msBWyTzU0 ________________________________________ From: R-sig-phylo [r-sig-phylo-boun...@r-project.org] on behalf of Bryan McLean [bryansmcl...@gmail.com] Sent: Tuesday, May 10, 2016 1:24 PM To: r-sig-phylo@r-project.org Subject: [R-sig-phylo] simulating continuous data Hi list, I’m working to simulate multiple continuous characters on a known phylogeny (using several of the standard models), and I want to compare properties of the simulated datasets to an empirical dataset. My question is: what is the standard method for ensuring that those datasets (simulated, empirical) are actually directly comparable, i.e. scaled similarly? Does this involve specifying a sensible root state (e.g. ancestral reconstruction) OR just rescaling one or the other datasets before or after the analysis? Forgive me if this is a bit of a naive question, just trying to get a sense of standard practices. -Bryan McLean _______________________________________________ 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/ _______________________________________________ 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/
