JAGS_fit {BayesTools}

Fits a 'JAGS' model

Description

A wrapper around run.jags that simplifies fitting 'JAGS' models with usage with pre-specified 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

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

chains

number of chains to be run, defaults to 4

adapt

number of samples used for adapting the MCMC chains, defaults to 500

burnin

number of burnin iterations of the MCMC chains, defaults to 1000

sample

number of sampling iterations of the MCMC chains, defaults to 4000

thin

thinning interval for the MCMC samples, defaults to 1

autofit

whether the models should be refitted until convergence criteria specified in autofit_control. Defaults to FALSE.

autofit_control

a list of arguments controlling the autofit function. Possible options are:

max_Rhat

maximum R-hat error for the autofit function. Defaults to 1.05.

min_ESS

minimum effective sample size. Defaults to 500.

max_error

maximum MCMC error. Defaults to 1.01.

max_SD_error

maximum MCMC error as the proportion of standard deviation of the parameters. Defaults to 0.05.

max_time

list specifying the time time and units after which the automatic fitting function is stopped. The units arguments need to correspond to units passed to difftime function.

sample_extend

number of samples between each convergence check. Defaults to 1000.

restarts

number of times new initial values should be generated in case the model fails to initialize. Defaults to 10.

parallel

whether the chains should be run in parallel FALSE

cores

number of cores used for multithreading if parallel = TRUE, defaults to chains

silent

whether the function should proceed silently, defaults to TRUE

seed

seed for random number generation

add_parameters

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

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 NULL.

fit

a 'BayesTools_fit' object (created by JAGS_fit() function) to be extended

Value

JAGS_fit returns an object of class 'runjags' and 'BayesTools_fit'.

See Also

JAGS_check_convergence()

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)