Hi all, Dear Louis, I suggest you to perform a bootstrapped cluster analysis on your data (try on Procrustes distances, or on Rel. Warp scores). This type of cluster analysis helps to identify statistically robust group (=clusters); the main reference is Pillar, 1999, 2001 (Pillar, V.D., 1999. How sharp are classifications? Ecology 80, 25082516; Pillar, V.D., 2001. MULTIV 2.1.1. Program, Porto Alegre.). You can check significance of a given partition (i.e. four groups) by means of resampling procedure. For a potential useful application see: Raia, Piras, Kotsakis. 2006. Detection of Plio-Quaternary large mammal communities of Italy: integration to biochronology. Quatern. Sci. Rev. London, 25: 846-854. You can download the reference at this web site: http://host.uniroma3.it/laboratori/paleontologia/Piras_vert.htm
Best Paolo Piras, Ph.D. I would like to simply point that PCA knows nothing > about groups. It > merely provides a lower dimensional representation of > higher dimensional > data that maximizes representation of total variance. > The appearance of > groups separations along, say, PC1, often occurs, but > is in no way > designed into the method. > > If you have clusters, then MANOVA, preferably of the > nonparametric > variety, will tell you if there are significant > differences between the > predefined groups, and other methods like CVA will > give an optimal > low-dimensional representation of those group > differences. > > Get thee to any decent multivariate text book, e.g., > Krazanowski's Principles of Multivariate Analysis or > many others, and > now might be a good time to start learning R > (http://www.r-project.org). > > Are you using tps programs? If so, you can do the > MANOVA with tpsREGRE - > regress shape on g-1 dummy variables encoding groups. > The excellent help > file should have the instructions. > > Best, dslice > > morphmet wrote: >> Dear morphometricians, >> >> I have the following problem: >> >> I have performed a PCA of shape (relative warps >> analysis) on a set of >> mouse mandibles from animals of different geographic >> origins. Now in a >> plot of PC1 vs PC2, I can "see" that PC1 sorts >> specimens into broadly >> overlapping clusters corresponding to the respective >> origins of the >> mice, while PC2 (and the other PCs) do not. The >> problem is now that the >> overlaps of the "population" clusters are rather >> broad so the question >> is how different they actually are. Also, I have >> several "populations", >> so it looks like a continuum of overlapping clouds. >> Could you recommend a means to quantify and/or >> somehow test the actual >> differences between "populations" along PC1? >> >> Louis Boell >> >> ------------------------------------------------------------------------ >> Machen Sie Ihre E-Mail-Kontakte zu >> Messenger-Freunden! Einfacher >> Adressimport! >> <http://redirect.gimas.net/?cat=hmtl&n=M1007AI&d=http://messenger.live.de/ersteschritte_adressimport.html> >> > > -- > Dennis E. Slice > Department of Anthropology > University of Vienna > ======================================================== > > > > -- > Replies will be sent to the list. > For more information visit > http://www.morphometrics.org > > -- Replies will be sent to the list. For more information visit http://www.morphometrics.org
