Description Usage Arguments Value Author(s) References Examples
View source: R/tess.PathSampling.R
tess.pathSampling uses a power posterior series and path-sampling to estimate the marginal likelihood of a model. This is a very general implementation of this algorithm which can be applied basically to any model. More information can be obtained in the vignette about how to apply this method.
1 2 | tess.pathSampling(likelihoodFunction,priorFunction,parameters,logTransforms,
iterations,burnin=round(iterations/3),K=50)
|
likelihoodFunction |
The log-likelihood function which will be called internally by likelihoodFunction(parameters). |
priorFunction |
A list of functions of the log-prior-densities of each parameter. |
parameters |
The initial parameter value list. |
logTransforms |
A vector of booleans telling if log-transform for the parameters should be used (e.g. for rates). |
iterations |
The number of iterations for the MCMC. |
burnin |
The number of iterations to burn before starting the MCMC. |
K |
The number of stepping stones. |
Returns the posterior samples for the parameters.
Sebastian Hoehna
Lartillot, N. and Philippe, H., 2006: Computing Bayes factors using thermodynamic integration. Systematic Biology, 55, 195
Baele et al., 2012: Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty
Baele et al., 2013: Accurate Model Selection of Relaxed Molecular Clocks in Bayesian Phylogenetics
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # load a test data set
data(cettiidae)
# convert the phylogeny into the branching times
times <- as.numeric( branching.times(cettiidae) )
# construct a likelihood function taking in a vector of parameters
likelihood <- function(params) {
# We use the parameters as diversification rate and turnover rate.
# Thus we need to transform first
b <- params[1] + params[2]
d <- params[2]
lnl <- tess.likelihood(times,b,d,samplingProbability=1.0,log=TRUE)
return (lnl)
}
# next, create the prior density functions
prior_diversification <- function(x) { dexp(x,rate=0.1,log=TRUE) }
prior_turnover <- function(x) { dexp(x,rate=0.1,log=TRUE) }
priors <- c(prior_diversification,prior_turnover)
# Note, the number of iterations, the burnin
# and the number of stepping stones is too small here
# and should be adapted for real analyses
marginalLikelihood <- tess.pathSampling( likelihood,
priors,
runif(2,0,1),
c(TRUE,TRUE),
10,
10,
K=4)
|
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