Hi Andres.
To answer your question at its face value, the line of code you want is:
anc<-setNames(apply(ANC$lik.anc,1,function(x)
names(which.max(x))),length(tree$tip.label)+1:tree$Nnode)
(The setNames ensures that your resultant vector gets the node numbers
for names.)
This message really deserves a more lengthy response for two reasons:
1) The scaled likelihoods returned by ace(...,type="discrete") are
actually conditional likelihoods of the subtree obtained via the pruning
algorithm of Felsenstein. These should generally not be used for
ancestral state estimation (we should use the marginal or joint
reconstructions instead).
2) Extracting only the most likely state as *the* ancestral state at the
node ignores the uncertainty of our estimate. Really we have a
probability distribution for the states at each node.
- Liam
Liam J. Revell, Assistant Professor of Biology
University of Massachusetts Boston
web: http://faculty.umb.edu/liam.revell/
email: liam.rev...@umb.edu
blog: http://blog.phytools.org
On 3/20/2013 2:23 PM, Andrés Parada wrote:
Sorry if this was addressed before.
I reconstructed a binary discrete character with* ace* (coded as 0-1).
As suggested in APER 2nd edition with the* Sylvia *case I can obtain the
"lik.anc" values but I wanted to derive the ancestral states from this
matrix.
*First*
*ANC <- ace (data$character, tree, type="d", method="ML", model="ARD")*
ANC$lik.anc look like this:
0 1
[1,] 9.999185e-01 8.147752e-05
*So I applied the same strategy as in the example mentioned above:*
*anc <- apply(ANC$lik.anc, 1, which.max)*
This returned a matrix of with values of 1 or 2.
Is there a way to modify the formula and sort this so it returns only 0 or
1 depending on which state has higher probability?
Thank you very much for your help.
[[alternative HTML version deleted]]
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