Hello Thibaut,
Thank you for these clarifications.
About 2: I understand how to use the abouheif test to detect the
phylogenetic signal (up to now I used it with the patristic matrix of
proximities). But I don't know how to use it to test the anti-signal.
Absence of signal is not anti-signal, or missed something?
Cheers
François
Hello François,
1. In pPCA, the sum of the eigenvalues is often meaningless, because it can be
a mixture of large positive and negative values. So this ratio is no longer
relevant. Selection of eigenvalues can be based on the amount of variance and
autocorrelation (Moran's I) represented (each eigenvalue is a product of the
two). See summary.ppca and screeplot.ppca.
2. The best way is testing positive/negative phylogenetic autocorrelation ("global/local"
structures in the paper's terminology) beforehand. See section 3.1 of the vignette
"Quantifying and testing phylogenetic signal" - abouheif.moran will test all variables at
once (just make sure to use the same measure of proximity in the pPCA).
3. As you suspected, testing phylogenetic signal of pPCA components is
meaningless, as these synthetic variables are already optimized for
phylogenetic signal. Estimating ancestral states is always possible; I can see
at least two ways of doing it: a) reconstruct the ancestral state of every
traits, and then compute the coordinates of the nodes on the pPCA axis using
the loadings of the analysis. In this case, nodes are used as 'supplementary
individuals'. b) reconstruct directly the principal component of pPCA; in this
case, the component needs to have a clear-cut interpretation.
Cheers
Thibaut
________________________________________
From: [email protected] [[email protected]] on
behalf of Francois KECK [[email protected]]
Sent: 11 February 2013 10:05
To: [email protected]
Subject: [R-sig-phylo] Some questions about pPCA
Dear all,
I'm a new subscriber to this list since I just started to play with
phylogenetic data with R. The task is facilitated by reading the
excellent book of E. Paradis. However I recently discovered the pPCA
method (as introduced by Jombart et al. 2010) and i'm very interested in
it to work on phylogenetic signal but I still have some questions...
1. I'm a long time user of ade4 to perform multivariate analysis. For a
classic PCA I usually calculate the % of variance taking account by each
axis using :
myPCA$eig/sum(myPCA$eig) * 100
I'd just like to be sure I can do the same with a pPCA, using absolute
values of the eigenvalues, e.g.:
abs(myPPCA$eig)/sum(abs(myPPCA$eig)) * 10
2. In their paper, Jombart et al. present some figures where they
sometimes exclude directly the local or the global principal component
because they know it doesn't exist (these are simulated data). Is there
a way to test the global vs the local component with "real data"? With
my own data I have a very low "local eigenvector" so I wonder if I could
only focus on global structure. Can I justify this choice with statistics?
3. I think it could be interesting to play with the species coordinates
especially with the global component. But does it make sense to assess
the phylogenetic signal or to estimate ancestral characters on these
constrained data? I'm a little doubtful about that and your point of
view is welcome.
Thank you for your help.
François KECK
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