That seems like it would be OK, at least if you think it is OK for 
nonphylogenetic PCA.
An alternative is to simulate data along your phylogeny, analyze it the same 
way, do it a couple thousand times, then make an empirical null distribution 
of, say, the eigenvalues when the data have no correlation on average but 
increased variance in the values of correlations caused by the phylogenetic 
hierarchy.  
This is discussed in our very old PHYLOGR package.
However, you will need to make some decisions about the "branch lengths" to use 
for your individuals within species, represented by a bunch of mini-star 
phylogenies.

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
http://scholar.google.com/citations?hl=en&user=iSSbrhwAAAAJ

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 Franck Stefani [fopstef...@gmail.com]
Sent: Wednesday, August 22, 2012 3:50 PM
To: r-sig-phylo@r-project.org
Subject: [R-sig-phylo] pPCA - global and local components

Hi,

Among the graphical outputs of the pPCA, there is the scree plot showing
the global and local components. I would like to know what are the criteria
to define the number of GPC or LPC to interpret ? Can we use a broken stick
model?

Cheers,

Franck

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