# NOT RUN {
# 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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