Joe Felsenstein wrote on 23/11/2012 02:05:
Emmanuel Paradis wrote:
If you are Bayesian, the trees sampled from an MCMC are here for estimation
including of the branch lengths, so you use them to compute some sort of
consensus topology as well as its branch lengths. So it makes sense that
MrBayes can do a consensus tree with branch lengths.
I endorse the rest of Emmanuel's advice but let me quibble with this one. The
posterior on trees may not consist mostly of trees varying around a single
consensus. If the posterior had, for example, two modes, each centered around
a different tree, a single consensus tree might not be appropriate, and branch
lengths computed by averaging lengths over the two modes might not be a good
guide to what the trees in the posterior looked like. I don't know enough
about MrBayes features to know whether they have some way around this.
There is a similar issue with parsimony methods -- the set of most parsimonious
trees may have a consensus, which may well not be a most parsimonious tree.
People who see the consensus of most parsimonious trees may not realize that
the particular tree they are looking at is not most parsimonious.
Indeed. A Lento plot may be a good way to detect this kind of problems.
It is in the package phangorn (see ?lento). The consensus network
(?consensusNet in the same package) is an alternative graphical method
to represent simultaneously incompatible splits. In general, I would
recommend to use these tools before going on with the consensus tree or
a tree with bootstrap values.
Best,
Emmanuel
J.F.
----
Joe Felsenstein j...@gs.washington.edu
Department of Genome Sciences and Department of Biology,
University of Washington, Box 355065, Seattle, WA 98195-5065 USA
(from 1 October 2012 to 10 December 2012 on sabbatical leave at)
Department of Statistics, University of California, Berkeley, 367 Evans Hall,
Berkeley, CA 94710
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--
Emmanuel Paradis
IRD, Jakarta, Indonesia
http://ape.mpl.ird.fr/
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