us mentioned references illustrated with
> > some simulations.
> > Hope it may helps...
> >
> > Best,
> >
> > Julien
> >
> > --
> > *De :* R-sig-phylo de la part de
> > Andrew Hipp
> > *Envoyé :* dim
p
> *Envoyé :* dimanche 26 août 2018 05:36
> *À :* bome...@utk.edu
> *Cc :* mailman, r-sig-phylo; Agus Camacho
> *Objet :* Re: [R-sig-phylo] understanding variance-covariance matrix
>
> I'll second Brian's self-citation. O'Meara et al. 2006 is I think one of
> the best introdu
trated with some
simulations.
Hope it may helps...
Best,
Julien
De : R-sig-phylo de la part de Andrew Hipp
Envoy� : dimanche 26 ao�t 2018 05:36
� : bome...@utk.edu
Cc : mailman, r-sig-phylo; Agus Camacho
Objet : Re: [R-sig-phylo] understanding variance-cova
I'll second Brian's self-citation. O'Meara et al. 2006 is I think one of
the best introductions to the phylogenetic covariance matrix, and I often
direct students to it.
Brian's point about the relationship between observed and expected
covariance is illustrated here in a brief note I wrote up
Hi Agus & Brian,
To complete Brian's response, vcv() is a generic function which works
with trees (class "phylo") and phylogenetic correlation structures
(class "corPhyl"). The latter can be used to define an OU model, see
?vcv, and ?corPhyl for the correlation structures available in ape.
Hi, Agus. The variance-covariance matrix comes from the tree and the
evolutionary model, not the data. Each entry between taxa A and B in the
VCV is how much covariance I should expect between data for taxa A and B
simulated up that tree using that model. I don't want to be *that guy*, but
O'Meara
