tess.PosteriorPrediction: tess.PosteriorPrediction: Approximation of the posterior...

In hoehna/TESS: Diversification Rate Estimation and Fast Simulation of Reconstructed Phylogenetic Trees under Tree-Wide Time-Heterogeneous Birth-Death Processes Including Mass-Extinction Events

Description

tess.PosteriorPrediction calls the simulation function exactly once for each sampled parameter combination. In that way, posterior predictive simulations can be obtained which then in turn can be used to compute summary statistics based on these posterior predictive simulations. Fore more information see the vignette.

Usage

tess.PosteriorPrediction(simulationFunction,parameters,burnin)

Arguments

simulationFunction	The simulation function which will be called internally by simulationFunction(parameters).
parameters	A matrix of parameters where the rows represent samples of parameters and the column the different parameters.
burnin	The fraction of samples to be discarded as burnin. This is 0.25 by default

Value

Returns samples simulated from the posterior predictive distribution.

Author(s)

Sebastian Hoehna

References

S. Hoehna: Fast simulation of reconstructed phylogenies under global, time-dependent birth-death processes. 2013, Bioinformatics, 29:1367-1374

Examples

# We first run an MCMC to obtain samples from the posterior distribution 
# and then simulate the posterior predictive distribution.

# The bird phylogeny as the test data set
data(cettiidae)
times <- as.numeric( branching.times(cettiidae) )

# The log-likelihood function
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)
}

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 and the burnin is too small here 
# and should be adapted for real analyses
samples <- tess.mcmc(likelihood,priors,c(1,0.1),c(TRUE,TRUE),c(0.1,0.1),10,10)

tmrca <- max(branching.times(cettiidae))
# The simulation function
sim <- 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]
  
  tree <- tess.sim.age(n=1,age=tmrca,b,d,samplingProbability=1.0)[[1]]
  return (tree)
}

trees <- tess.PosteriorPrediction(sim,samples)

# compute the posterior predictive test statistic
ppt <- tess.PosteriorPredictiveTest(trees,cettiidae,gammaStat)
# get the p-value of the observed test-statistic
ppt[[2]]

hoehna/TESS documentation built on Feb. 3, 2022, 5:59 a.m.