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Subject: Canonical variates from first PCs of GPA residuals
Date: Tue, 10 Feb 2009 05:15:05 -0800 (PST)
From: Peter Taylor <[email protected]>
To: <[email protected]>
Dear Morphometricians
I am working with data where the number of landmarks (from rodent
skulls) exceeds the smallest sample sizes of my groups. To circumvent
statistical problems with null determinants when using canonical
analysis (CVA) of the weights matrix from GPA, is it permissable to
conduct CVA on the first few PCs from a PCA of the residuals, or aligned
coordinates after least squares, GPA? If so how does one objectively
decide how many PCs to include, should this number be less than the
smallest group sample size, or should it depend on a certain threshold
of cumulative explained variance (70%) or on the eigenvalues (>1?), or
on the degree of separation of groups? Also, is this approach
equivalent, or preferable, to conducting CVA on the first few relative
warps from a relative warps analysis (PCA of weights matrix). I have
seen both approaches in the literature but not sure which is best.
Many thanks
Peter
Dr Peter John Taylor
Curator of Mammals
Durban Natural Science Museum
Ethekwini Libraries & Heritage
P O Box 4085
Durban
4000
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