Yes, I meant negative phylogenetic autocorrelation. With my data,
permutations tests detect positive autocorrelation, no doubt on that.
But there may exist overdispersion between some phylogenetically close
species. So I would like to compare the global and local components.
Maybe I can just say that there is 2.5 times more variability taking
account by the global component.
== pPCA eigenvalues decomposition ==
eig var moran
Axis 1 119.8040 652.7595 0.1835347
Axis 4 -27.1536 238.8116 -0.1137030
Cheers
François
> Hello,
>
> I don't know what anti-signal is. Do you mean negative phylogenetic
> autocorrelation?
>
> When there is negative autocorrelation, Moran's index / Abouheif's statistic
> is smaller than permuted values (use 'plot' on the krandtest object).
>
> Cheers
> Thibaut
>
> ________________________________________
> From: [email protected] [[email protected]]
> on behalf of Francois KECK [[email protected]]
> Sent: 11 February 2013 13:47
> To: [email protected]
> Subject: Re: [R-sig-phylo] Some questions about pPCA
>
> 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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>>
>>
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