jagsUI: Call JAGS from R
In kenkellner/jagsUI2: A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses

Description Usage Arguments Value Examples

The jagsUI function is a basic user interface for running JAGS analyses via package rjags inspired by similar packages like R2WinBUGS, R2OpenBUGS, and R2jags. The user provides a model file, data, initial values (optional), and parameters to save. The function compiles the information and sends it to JAGS, then consolidates and summarizes the MCMC output in an object of class jagsUI.

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jagsUI(model_file, data, inits = NULL, params, n_cores = 1, n_chains,
  n_adapt = 100, n_iter, n_warmup = 0, n_thin = 1,
  modules = c("glm", "dic"), factories = NULL, quiet = FALSE)

`model_file`	Path to file containing the model written in `BUGS` code.
`data`	A named list of the data objects required by the model.
`inits`	A list with `n.chains` elements; each element of the list is itself a list of starting values for the `BUGS` model, or a function creating (possibly random) initial values. If inits is `NULL`, `JAGS` will generate initial values for parameters.
`params`	Character vector of the names of the parameters in the model which should be monitored.
`n_cores`	If > 1, the number of parallel CPU cores to use. MCMC chains are split between cores.
`n_chains`	Number of Markov chains to run.
`n_adapt`	Number of iterations to run in the adaptive phase.
`n_iter`	Total number of iterations per chain (including warm-up).
`n_warmup`	Number of iterations at the beginning of each chain to discard. Does not include the adaptive iterations.
`n_thin`	Thinning rate. Must be a positive integer.
`modules`	List of JAGS modules to load before analysis.
`factories`	Optional character vector of factories to enable or disable, in the format <factory> <type> <setting>. For example, to turn `TemperedMix` on you would provide `'mix::TemperedMix sampler TRUE'` (note spaces between parts). Make sure you have the corresponding modules loaded as well.
`quiet`	If `TRUE`, suppress console output.

An object of class jagsUI. Notable elements in the output object include:

`samples`	The original output object from the `rjags` package, as class `mcmc.list`.
`summary`	A summary of various statistics calculated based on model output, in matrix form.
`model`	The `rjags` model object; this will contain multiple elements if `n_cores > 1`.

 #Analyze Longley economic data in JAGS 
 #Package data
 data(longley)
 data <- list(gnp=longley$GNP, employed=longley$Employed, 
              n=length(longley$Employed))

 #Write model file
 model_file <- tempfile()
 writeLines("
 model{
 #Likelihood
 for (i in 1:n){ 
   employed[i] ~ dnorm(mu[i], tau)     
   mu[i] <- alpha + beta*gnp[i]
 }
 #Priors
 alpha ~ dnorm(0, 0.00001)
 beta ~ dnorm(0, 0.00001)
 sigma ~ dunif(0,1000)
 tau <- pow(sigma,-2)
 }
 ", con=model_file)

#Inits function
 inits <- function(){  
   list(alpha=rnorm(1,0,1),beta=rnorm(1,0,1),sigma=runif(1,0,3))  
 }

#Parameters to save posteriors for
params <- c('alpha','beta','sigma')

#Run analysis
out <- jagsUI(model_file, data, inits, params, 
            n_chains=3, n_iter=1000, n_warmup=500)

#Take another 1000 posterior samples
out <- update(out, n_iter=1000)