Thank you very much to everyone that replied. R-sig-phylo, as usual, a very
helpful and friendly community! I got everything to work now. Jonathan
Drury also replied off-list with a similar approach in function form.
Cheers,
Rafael
*--*
*Rafael Sobral Marcondes*
PhD Candidate (Systematics,
Graham's right of course. Sorry about that.
You might do something like:
Si<-(Xi-matrix(1,nrow(Xi),1)%*%phyl.vcv(Xm,vcv(tree),
1)$alpha[,1])%*%pca$Evec
I also agree with Joe that you can take the phylogeny into account
whilst accounting for sampling error using his approach or that of
Hi Rafael,
You need to mean-center your traits before multiplying by the matrix of
eigenvectors. Compute the vector of phylogenetic means (under BM or Pagel’s
lambda), subtract each value from the relevant column of Xm and then compute
Si. The result should be identical to the scores from your
Rafael and Liam --
>
> So far as I know, there is currently no way to explicitly take into account
> sampling error in computing principal components while also accounting for
> the phylogeny. However, it is relatively straightforward to compute scores
> for individuals from a PCA conducted on
Hi Rafael.
So far as I know, there is currently no way to explicitly take into
account sampling error in computing principal components while also
accounting for the phylogeny. However, it is relatively straightforward
to compute scores for individuals from a PCA conducted on species means.
Dear all,
Does anyone have any advice on how to calculate measurement error in an
analysis using phylogenetic principal components? Or, in other words, after
I run a phylogenetic PCA on species-level data, how can I "project" my
individual-level data into the phylogenetic PCs so I can calculate a
