calculate_marginal_log_likelihood {AnaCoDa} | R Documentation |
initializes the model object.
calculate_marginal_log_likelihood(parameter, mcmc, mixture, n.samples, divisor)
parameter |
An object created with |
mcmc |
An object created with |
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 |
calculate_marginal_log_likelihood 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
This function returns the model object created.
## 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)