You can test for anti-signal using the randomization procedures of: Blomberg, S. P., T. Garland, Jr., and A. R. Ives. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57:717-745.
See page 719. 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 = Dept. office (not confidential) Email: tgarl...@ucr.edu http://www.biology.ucr.edu/people/faculty/Garland.html http://scholar.google.com/citations?hl=en&user=iSSbrhwAAAAJ Inquiry-based Middle School Lesson Plan: "Born to Run: Artificial Selection Lab" http://www.indiana.edu/~ensiweb/lessons/BornToRun.html ________________________________________ From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org] on behalf of Francois KECK [francois.k...@thonon.inra.fr] Sent: Monday, February 11, 2013 5:47 AM To: r-sig-phylo@r-project.org Subject: Re: [R-sig-phylo] Some questions about pPCA Hello Thibaut, Thank you for these clarifications. About 2: I understand how to use the abouheif test to detect the phylogenetic signal (up to now I used it with the patristic matrix of proximities). But I don't know how to use it to test the anti-signal. Absence of signal is not anti-signal, or missed something? Cheers François > Hello François, > > 1. In pPCA, the sum of the eigenvalues is often meaningless, because it can > be a mixture of large positive and negative values. So this ratio is no > longer relevant. Selection of eigenvalues can be based on the amount of > variance and autocorrelation (Moran's I) represented (each eigenvalue is a > product of the two). See summary.ppca and screeplot.ppca. > > 2. The best way is testing positive/negative phylogenetic autocorrelation > ("global/local" structures in the paper's terminology) beforehand. See > section 3.1 of the vignette "Quantifying and testing phylogenetic signal" - > abouheif.moran will test all variables at once (just make sure to use the > same measure of proximity in the pPCA). > > 3. As you suspected, testing phylogenetic signal of pPCA components is > meaningless, as these synthetic variables are already optimized for > phylogenetic signal. Estimating ancestral states is always possible; I can > see at least two ways of doing it: a) reconstruct the ancestral state of > every traits, and then compute the coordinates of the nodes on the pPCA axis > using the loadings of the analysis. In this case, nodes are used as > 'supplementary individuals'. b) reconstruct directly the principal component > of pPCA; in this case, the component needs to have a clear-cut interpretation. > > Cheers > > Thibaut > > ________________________________________ > From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org] > on behalf of Francois KECK [francois.k...@thonon.inra.fr] > Sent: 11 February 2013 10:05 > To: r-sig-phylo@r-project.org > Subject: [R-sig-phylo] Some questions about pPCA > > Dear all, > I'm a new subscriber to this list since I just started to play with > phylogenetic data with R. The task is facilitated by reading the > excellent book of E. Paradis. However I recently discovered the pPCA > method (as introduced by Jombart et al. 2010) and i'm very interested in > it to work on phylogenetic signal but I still have some questions... > > 1. I'm a long time user of ade4 to perform multivariate analysis. For a > classic PCA I usually calculate the % of variance taking account by each > axis using : > myPCA$eig/sum(myPCA$eig) * 100 > I'd just like to be sure I can do the same with a pPCA, using absolute > values of the eigenvalues, e.g.: > abs(myPPCA$eig)/sum(abs(myPPCA$eig)) * 10 > > 2. In their paper, Jombart et al. present some figures where they > sometimes exclude directly the local or the global principal component > because they know it doesn't exist (these are simulated data). Is there > a way to test the global vs the local component with "real data"? With > my own data I have a very low "local eigenvector" so I wonder if I could > only focus on global structure. Can I justify this choice with statistics? > > 3. I think it could be interesting to play with the species coordinates > especially with the global component. But does it make sense to assess > the phylogenetic signal or to estimate ancestral characters on these > constrained data? I'm a little doubtful about that and your point of > view is welcome. > > Thank you for your help. > > François KECK > > _______________________________________________ > 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/ _______________________________________________ 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/
