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This repository includes Bayesian implementations of the MuSSE and ClaSSE methods. The use of (half-Cauchy or Exponential) hyper-priors on the rate parameters allows to control for overparameterization.

dsilvestro/mcmc-diversitree

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mcmc-diversitree

This repository includes Bayesian implementations of State Speciation and Extinction models, including MuSSE, GeoSSE and ClaSSE. The use of (half-Cauchy or Exponential) hyper-priors on the rate parameters allows to control for over-parameterization. The mcmc-SSE.R script uses exponential priors on the rate parameters with a gamma hyper-prior.

Main commands

The script requires a tree file (NEXUS format), a text file with the trait data (see example files), and specifying a model. The models currently available are "musse", "classe", "geosse".

RScript mcmc-SSE.R example_files/bromelioieae_consensus.tre example_files/traitGeoSSE.txt geosse

The taxon sampling (fraction of sampled species out of the total) is provided for each character state using the flag --rho. For example --rho "0.5 0.4 1" in a geosse model specifies a 50%, 40% sampling for species in area 1 and 2, respectively, and a complete sampling for widespread species.

RScript mcmc-SSE.R example_files/bromelioieae_consensus.tre example_files/traitGeoSSE.txt geosse --rho "0.5 0.4 1"

Additional options are:

--i: number of MCMC iterations
--s: sampling frequency
--p: print frequency

Requirements

R libraries: ape, optparse, picante, diversitree all available on CRAN.

References

mcmc-diversitree:

Silvestro et al. 2014 Evolution
Burin et al. 2016 Nature Comm

diversitree library:

FitzJohn et al. 2008 Syst Biol
GitHub repository

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This repository includes Bayesian implementations of the MuSSE and ClaSSE methods. The use of (half-Cauchy or Exponential) hyper-priors on the rate parameters allows to control for overparameterization.

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