BayesTools (version 0.2.18)

JAGS_bridgesampling: Compute marginal likelihood of a 'JAGS' model

Description

A wrapper around bridge_sampler that automatically computes likelihood part dependent on the prior distribution and prepares parameter samples. log_posterior must specify a function that takes two arguments - a named list of samples from the prior distributions and the data, and returns log likelihood of the model part.

Usage

JAGS_bridgesampling(
  fit,
  log_posterior,
  data = NULL,
  prior_list = NULL,
  formula_list = NULL,
  formula_data_list = NULL,
  formula_prior_list = NULL,
  add_parameters = NULL,
  add_bounds = NULL,
  maxiter = 10000,
  silent = TRUE,
  ...
)

Value

JAGS_bridgesampling returns an object of class 'bridge'.

Arguments

fit

model fitted with either runjags posterior samples obtained with rjags-package

log_posterior

function that takes a named list of samples, the data, and additional list of parameters passed as ... as input and returns the log of the unnormalized posterior density of the model part

data

list containing data to fit the model (not including data for the formulas)

prior_list

named list of prior distribution (names correspond to the parameter names) of parameters not specified within the formula_list

formula_list

named list of formulas to be added to the model (names correspond to the parameter name created by each of the formula)

formula_data_list

named list of data frames containing data for each formula (names of the lists correspond to the parameter name created by each of the formula)

formula_prior_list

named list of named lists of prior distributions (names of the lists correspond to the parameter name created by each of the formula and the names of the prior distribution correspond to the parameter names) of parameters specified within the formula

add_parameters

vector of additional parameter names that should be used in bridgesampling but were not specified in the prior_list

add_bounds

list with two name vectors ("lb" and "up") containing lower and upper bounds of the additional parameters that were not specified in the prior_list

maxiter

maximum number of iterations for the bridge_sampler

silent

whether the progress should be printed, defaults to TRUE

...

additional argument to the bridge_sampler and log_posterior function

Examples

if (FALSE) {
# simulate data
set.seed(1)
data <- list(
  x = rnorm(10),
  N = 10
)
data$x

# define priors
priors_list <- list(mu = prior("normal", list(0, 1)))

# define likelihood for the data
model_syntax <-
  "model{
    for(i in 1:N){
      x[i] ~ dnorm(mu, 1)
    }
  }"

# fit the models
fit <- JAGS_fit(model_syntax, data, priors_list)

# define log posterior for bridge sampling
log_posterior <- function(parameters, data){
  sum(dnorm(data$x, parameters$mu, 1, log = TRUE))
}

# get marginal likelihoods
marglik <- JAGS_bridgesampling(fit, log_posterior, data, priors_list)
}