The issue of collinearity of independent variables is neither better nor worse with PGLS as opposed to OLS. Statistical significance per se of a correlation between X variables is not really the issue. How strong is the correlation? Most sources suggest that it needs to be greater than 0.7-0.8 in magnitude to cause serious problems.
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 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 Xu Han [duck_han365...@hotmail.com] Sent: Friday, August 17, 2012 12:33 PM To: r-sig-phylo@r-project.org Subject: [R-sig-phylo] Can PGLS cope with collinearity between explanatory variables? Hi all, I am testing a correlation between two explanatory variables and a response variable using PGLS. All of the variables are continuous. My model is Log female body size ~ Log egg size * Log clutch size. However, there is a significant negative correlation between egg size and clutch size. Can PGLS cope with collinearity between explanatory variables? Is there any way that I can apply something like principal component analysis to PGLS models? Thanks, Xu [[alternative HTML version deleted]] _______________________________________________ R-sig-phylo mailing list R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo _______________________________________________ R-sig-phylo mailing list R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
