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.

JAGS_bridgesampling(
fit,
data,
prior_list,
log_posterior,
maxiter = 10000,
silent = TRUE,
...
)

## Arguments

fit model fitted with either runjags posterior samples obtained with rjags-package data that were used to fit the model named list of prior distribution (names correspond to the parameter names) 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 vector of additional parameter names that should be used in bridgesampling but were not specified in the prior_list 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 maximum number of iterations for the bridge_sampler whether the progress should be printed, defaults to TRUE additional argument to the bridge_sampler and log_posterior function

## Value

JAGS_bridgesampling returns an object of class 'bridge'.

## 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, data, priors_list, log_posterior)
}