----- Forwarded message from "F. James Rohlf" -----
Date: Mon, 11 Jun 2012 09:30:52 -0400
From: "F. James Rohlf"
Reply-To: [email protected]
Subject: Re: random skewers and allometry
To: Morphmet
Just a quick response because I am traveling right now.
The random skewers method is just a very inefficient method to compare
directions of the first PC axes. CPCA analyses can provide much more
information though it seems unlikely that all PCs could be parallel in two or
more groups.
-------
Sent remotely by F. James Rohlf,
John S. Toll Professor, Stony Brook University
-----Original Message-----
From: [email protected]
Date: Mon, 11 Jun 2012 00:29:50
To: <[email protected]>
Reply-To: [email protected]
Subject: random skewers and allometry
----- Forwarded message from Milos Blagojevic -----
Date: Fri, 8 Jun 2012 06:20:22 -0400
From: Milos Blagojevic
Reply-To: Milos Blagojevic
Subject: random skewers and allometry
To: morphmet
Dear Morphometricians,
Considering the ever-lasting question of size vs. shape variability in the
collections of linear measurements I came across these two contrasting papers.
1. Berner, D., 2011. Size correction in biology: how reliable are approaches
based on (common) principal component analysis? Oecologia 166, 961–971.
2. McCoy, M.W., Bolker, B.M., Osenberg, C.W., Miner, B.G., Vonesh, J.R., 2006.
Size correction: comparing morphological traits among populations and
environments. Oecologia 148, 547–554.
Both of them suggest that the decision on whether to factor-out size
variability should be made on the basis of inter-population comparison (if
there are multiple populations). My question is that common principal
components analysis, although providing covariance matrix similarity with
tests, could be substituted with random skewers method of Cheverud? Now in that
substitution we would lost CPC1 which could be used for, i.e. Burnaby`s back
projection (if all populations share the same size/shape relationship). Could
random skewers coefficient be used as a proxy of similarity in determining
whether major axes of variability run parallel or diverge or are the same? If
all of these coefficients be sufficiently high (although robust test is
lacking) would it be safe to assume that whole sample PC1 axis is a well-fit
representation of size variability, that could be used for either regression or
Burnaby projection?
Best regards,
Milos Blagojevic, Ph.D. student,
Department of Biology and Ecology,
Faculty of Science, Kragujevac, Serbia.
email: paulidealist.kg.ac.rs; [email protected]; [email protected]
----- End forwarded message -----
----- End forwarded message -----
