Estimate marginal likelihood by thermodynamic integration and Gauss-Legendre
quadrature from a sample of
n power posterior MCMC chains sampled
with mcmctree (or bpp).
gauss.quad(mcmcf = "mcmc.txt", betaf = "beta.txt")
character, mcmc output file name
character, file with beta values
The MCMC samples should be stored in a directory structure created
method = "gauss-quad". The function
will read the stored log-likelihood values and calculate the log-marginal
Numerical integration is done using Gauss-Legendre quadrature. See Rannala and Yang (2017) for details (also dos Reis et al. 2017, Appendix 2).
A list with components
logml, the log-marginal likelihood estimate;
se, the standard error of the estimate;
mean.logl, the mean of
log-likelihood values sampled for each beta; and
b, the beta values
Mario dos Reis
Rannala B and Yang Z. (2017) Efficient Bayesian species tree inference under the multispecies coalescent. Systematic Biology 66: 823-842.
dos Reis et al. (2017) Using phylogenomic data to explore the effects of relaxed clocks and calibration strategies on divergence time estimation: Primates as a test case. bioRxiv
make.bfctlf to prepare directories and mcmctree control files
to calculate the power posterior.
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