This implements a 2-parameter model, as in LAGRANGE or
bears_2param_standard_fast, but omits the
speciation/cladogenesis model. This means that the model
is purely continuous-time, as when biogeographic range is
treated as a discrete character in software designed for
inference on morphological () or molecular data (). This
model is that implemented in
BayArea, if no
distance-dependent effect on dispersal probability is
assumed. Such distance-dependence could easily be added
with a third parameter, however.
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The filename of the phylogenetic tree, in
Tipnames should match the names in geogfn. See
A PHYLIP-style file with geographic range
The maximum rangesize, in number of
areas. Having a smaller maximum range size means that
you can have more areas (the size of the state space is
greatly reduced; see
If >1, parallel processing will
be attempted. Note: parallel processing via
BayArea is a new program by Landis, Matzke, Moore,
and Huelsenbeck; see Landis et al. (2013).
However, BayArea does not currently implement
cladogenesis models; it only has continuous-time model
for evolutionary change along branches. In effect, this
means that the cladogenesis model is sympatric speciation
with complete range copying with probability 1.
bears_output A list of outputs.
Nicholas J. Matzke [email protected]
Felsenstein, Joe. The Newick tree format. http://evolution.genetics.washington.edu/phylip/newicktree.html http://phylo.wikidot.com/matzke-2013-international-biogeography-society-poster https://code.google.com/p/lagrange/
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test=1 # Get the example files directory extdata_dir = np(system.file("extdata", package="BioGeoBEARS")) # tmp hard code: # extdata_dir = "/Dropbox/_njm/__packages/BioGeoBEARS_setup/inst/extdata/" # Set the filenames (Hawaiian Psychotria from Ree & Smith 2008) trfn = np(paste(extdata_dir, "/Psychotria_5.2.newick", sep="")) tr = read.tree(file=trfn) geogfn = np(paste(extdata_dir, "/Psychotria_geog.data", sep="")) # Look at the tree and ranges, for kicks getranges_from_LagrangePHYLIP(lgdata_fn=geogfn) tr ## Not run: # Run the ML search bears_output = bears_2param_standard_fast_symOnly(trfn=trfn, geogfn=geogfn) bears_output ## End(Not run)
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