-------- Original Message -------- Subject: RE: PCA versus RWA Date: Tue, 1 Sep 2009 11:59:38 -0700 (PDT) From: F. James Rohlf <[email protected]> Reply-To: <[email protected]> Organization: Stony Brook University To: <[email protected]> References: <[email protected]> The partial warp scores represent a rigid rotation of the Procrustes shape coordinates within the tangent space. Assuming one has taken the step to first project the Procrustes shape coordinates into the tangent space then the projections of the specimens onto the PCA axes will be identical. That means there is no real advantage of computing partial warp scores unless it is on interest to see how much of the shape variation is at large versus small spatial scales. Well, perhaps one advantage - they give you the proper number of shape variables so that one does not have get the eigenvectors of an unnecessarily large matrix and then discard the last 4 (or 7 for 3D data) eigenvectors that have eigenvalues exactly equal to zero. ========================= F. James Rohlf Distinguished Professor, Stony Brook University http://life.bio.sunysb.edu/ee/rohlf
-----Original Message----- From: morphmet [mailto:[email protected]] Sent: Tuesday, September 01, 2009 10:46 AM To: morphmet Subject: PCA versus RWA -------- Original Message -------- Subject: PCA versus RWA Date: Tue, 1 Sep 2009 06:20:00 -0700 (PDT) From: Alexandra Wegmann <[email protected]> To: [email protected] Hello Could anybody tell me when one should simply do a PCA (or CVA) on the procrusted shape coordinates and when one should do the PCA (or CVA) on the partial warp scores (relative warp analysis)? Your answer would be highly appreciated. Thanks in advance Alexandra Master student University of Zurich E-Mail: [email protected] -- Replies will be sent to the list. For more information visit http://www.morphometrics.org
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