BioGeoBEARS (version 0.2.1)

get_ML_states_from_relprobs: Extract the ML states at each node, from a table of relative probabilities -- old version

Description

Given a table with the rows representing nodes, and the columns representing the relative probabilities of each state, this function finds the ML (maximum likelihood) state(s) for each node.

Usage

get_ML_states_from_relprobs(relprobs, statenames, returnwhat = "states", if_ties = "takefirst")

Arguments

relprobs

A numeric matrix of relative probabilities

statenames

The names of the states/geographic ranges (e.g., A, AB, CDE, ABD, etc...)

returnwhat

If "indices", return the 0-based indices of the states. If "states", return the name of the state, based on statenames.

if_ties

What to do with ties. Currently, the only option is to take the first (this will be shown in e.g. a pie chart, of course).

Value

ML_states or ML_states_indices, depending on returnwhat.

Details

If possible, the input matrix should be the actual ML estimate of the state probabilities at each node, rather than just the scaled conditional likelihoods at each node. The latter reflect only the tips-down information, whereas the former (the marginal ancestral state reconstruction) uses all of the information, and the probabilities of the states at the root and in the outgroup(s) can influence the estimates in the ingroups. This would not likely be particularly important in a pure continuous-time model, but in a model with cladogenesis it could matter quite a bit.

See http://blog.phytools.org/2013/03/marginal-ancestral-state-reconstruction.html for more discussion of marginal ancestral state reconstructions, versus mere scaled conditional likelihoods.

Revell and other sources (Felsenstein (2004)) advocate the "re-rooting" method for obtaining the marginal ancestral state reconstructions; however, re-rooting requires a time-reversible model and a tree with no root. In biogeography we have a non-reversible model, and typically a time-scaled chronogram. However, the same result can be obtained by modifying the scaled conditional likelihoods obtained from a downpass from the tips, via an doing an up-pass from the root scaled conditional likelihoods, being careful to transfer probabilities via the time-forward version of the Q-matrix and cladogenesis/speciation matrix.

Note: further notes as this is implemented (required!)

References

http://phylo.wikidot.com/matzke-2013-international-biogeography-society-poster http://blog.phytools.org/2013/03/marginal-ancestral-state-reconstruction.html https://code.google.com/p/lagrange/

Felsenstein2004

Matzke_2012_IBS

See Also

get_ML_state_indices

Examples

testval=1