JAGS_fit {BayesTools}  R Documentation 
Fits a 'JAGS' model
Description
A wrapper around run.jags that simplifies fitting 'JAGS' models with usage with prespecified model part of the 'JAGS' syntax, data and list of prior distributions.
Usage
JAGS_fit(
model_syntax,
data = NULL,
prior_list = NULL,
formula_list = NULL,
formula_data_list = NULL,
formula_prior_list = NULL,
chains = 4,
adapt = 500,
burnin = 1000,
sample = 4000,
thin = 1,
autofit = FALSE,
autofit_control = list(max_Rhat = 1.05, min_ESS = 500, max_error = 0.01, max_SD_error =
0.05, max_time = list(time = 60, unit = "mins"), sample_extend = 1000, restarts = 10),
parallel = FALSE,
cores = chains,
silent = TRUE,
seed = NULL,
add_parameters = NULL,
required_packages = NULL
)
JAGS_extend(
fit,
autofit_control = list(max_Rhat = 1.05, min_ESS = 500, max_error = 0.01, max_SD_error =
0.05, max_time = list(time = 60, unit = "mins"), sample_extend = 1000, restarts = 10),
parallel = FALSE,
cores = NULL,
silent = TRUE,
seed = NULL
)
Arguments
model_syntax 
jags syntax for 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 
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 
chains 
number of chains to be run, defaults to 
adapt 
number of samples used for adapting the MCMC chains, defaults to 
burnin 
number of burnin iterations of the MCMC chains, defaults to 
sample 
number of sampling iterations of the MCMC chains, defaults to 
thin 
thinning interval for the MCMC samples, defaults to 
autofit 
whether the models should be refitted until convergence criteria
specified in 
autofit_control 
a list of arguments controlling the autofit function. Possible options are:

parallel 
whether the chains should be run in parallel 
cores 
number of cores used for multithreading if 
silent 
whether the function should proceed silently, defaults to 
seed 
seed for random number generation 
add_parameters 
vector of additional parameter names that should be used
monitored but were not specified in the 
required_packages 
character vector specifying list of packages containing
JAGS models required for sampling (in case that the function is run in parallel or in
detached R session). Defaults to 
fit 
a 'BayesTools_fit' object (created by 
Value
JAGS_fit
returns an object of class 'runjags' and 'BayesTools_fit'.
See Also
Examples
## Not run:
# 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)
## End(Not run)