calculateMarginalLogLikelihood: Calculates the marginal log-likelihood for a set of...

In AnaCoDa: Analysis of Codon Data under Stationarity using a Bayesian Framework

Description

initializes the model object.

Usage

calculateMarginalLogLikelihood(
  parameter,
  mcmc,
  mixture,
  n.samples,
  divisor,
  warnings = TRUE
)

Arguments

parameter	An object created with initializeParameterObject.
mcmc	An object created with initializeMCMCObject
mixture	determines for which mixture the marginal log-likelihood should be calculated
n.samples	How many samples should be used for the calculation
divisor	A value > 1 in order to scale down the tails of the importance distribution
warnings	Print warnings such as when the variance of a parameter is 0, which might occur when parameter is fixed

Details

calculateMarginalLogLikelihood Calculate marginal log-likelihood for calculation of the Bayes factor using a generalized harmonix mean estimator of the marginal likelihood. See Gronau et al. (2017) for details

Value

This function returns the model object created.

Examples

## Not run: 
# Calculate the log-marginal likelihood
parameter <- loadParameterObject("parameter.Rda")
mcmc <- loadMCMCObject("mcmc.Rda")
calculate_marginal_likelihood(parameter, mcmc, mixture = 1, 
samples = 500, scaling = 1.5)

# Calculate the Bayes factor for two models
parameter1 <- loadParameterObject("parameter1.Rda")
parameter2 <- loadParameterObject("parameter2.Rda")
mcmc1 <- loadMCMCObject("mcmc1.Rda")
mcmc2 <- loadMCMCObject("mcmc2.Rda")
mll1 <- calculate_marginal_likelihood(parameter1, mcmc1, mixture = 1, 
samples = 500, scaling = 1.5)
mll2 <- calculate_marginal_likelihood(parameter2, mcmc2, mixture = 1, 
samples = 500, scaling = 1.5)
cat("Bayes factor: ", mll1 - mll2, "\n")

## End(Not run)
 

AnaCoDa documentation built on Jan. 8, 2021, 2:37 a.m.