Hi Jesse,
Do you want to select the trees that are most incongruent with some sort of
average of the tree distribution? The `treespace`and `phytools` packages
have functions that compute average trees with different metrics, and they
also allow you to measure the distance between each tree and
Hi Jesse,
In order to account for phylogenetic uncertainty you are better just
pulling trees from their posterior rather than choosing trees that are
incongruent. The latter will give estimates biased toward those
associated with extreme trees.
If the analysis is the binomial model you
That's definitely helpful, Joseph, but I'll need to extend the tips to
varying amounts.
Basically, I performed a tip-dating analysis using constraints based on the
FADs of a bunch of fossils.
However, now some of the analyses I want to perform require that the tips
extend to the species'
If you want to just extend all tips by a constant amount you can do this:
# extend terminal edges by arbitrary amount (here: 13)
idx <- which(phy$edge[,2] < (phy$Nnode + 1));
phy$edge.length[idx] <- phy$edge.length[idx] + 13;
HTH.
Joseph.
Joseph W. Brown
Hi Jesse,
there is a function maxCladeCred() to compute maximum-clade-credibility in
phangorn, if you prefer doing it in R. In the package are several distances
between trees implemented see ?RF.dist (treespace uses them internally).
Klaus
On Wed, Jul 26, 2017 at 11:06 AM, Santiago Sánchez <
Hi Jesse,
As Eduardo says, if in fact you want to see how different trees are from a
"consensus", something that you could try is find the
maximum-clade-credibility (MCC) tree (you can do this with treeAnnotator
from BEAST). This will be a fully bifurcating tree and is essentially the
tree in the
Awesome, thanks Joseph!
On Wed, Jul 26, 2017 at 9:10 AM, Joseph W. Brown wrote:
> I think this'll work, depending on how you store your data.
>
> # assumes a tree 'phy' and a dataframe, 'df', with first column of tip
> names, and second column of values
> for (i in
I think this'll work, depending on how you store your data.
# assumes a tree 'phy' and a dataframe, 'df', with first column of tip names,
and second column of values
for (i in 1:dim(df)[1]) {
# find index of edge length
idx <- which(phy$edge[,2] == which(phy$tip.label ==