Introduction to jagsUI

Installing JAGS

In addition to installing the jagsUI package, we also need to separately install the free JAGS software, which you can download here.

Once that’s installed, load the jagsUI library:

library(jagsUI)

Typical jagsUI Workflow

  1. Organize data into a named list
  2. Write model file in the BUGS language
  3. Specify initial MCMC values (optional)
  4. Specify which parameters to save posteriors for
  5. Specify MCMC settings
  6. Run JAGS
  7. Examine output

1. Organize data

We’ll use the longley dataset to conduct a simple linear regression. The dataset is built into R.

data(longley)
head(longley)
#      GNP.deflator     GNP Unemployed Armed.Forces Population Year Employed
# 1947         83.0 234.289      235.6        159.0    107.608 1947   60.323
# 1948         88.5 259.426      232.5        145.6    108.632 1948   61.122
# 1949         88.2 258.054      368.2        161.6    109.773 1949   60.171
# 1950         89.5 284.599      335.1        165.0    110.929 1950   61.187
# 1951         96.2 328.975      209.9        309.9    112.075 1951   63.221
# 1952         98.1 346.999      193.2        359.4    113.270 1952   63.639

We will model the number of people employed (Employed) as a function of Gross National Product (GNP). Each column of data is saved into a separate element of our data list. Finally, we add a list element for the number of data points n. In general, elements in the data list must be numeric, and structured as arrays, matrices, or scalars.

jags_data <- list(
  gnp = longley$GNP,
  employed = longley$Employed,
  n = nrow(longley)
)

2. Write BUGS model file

Next we’ll describe our model in the BUGS language. See the JAGS manual for detailed information on writing models for JAGS. Note that data you reference in the BUGS model must exactly match the names of the list we just created. There are various ways to save the model file, we’ll save it as a temporary file.

# Create a temporary file
modfile <- tempfile()

#Write model to file
writeLines("
model{

  # Likelihood
  for (i in 1:n){ 
    # Model data
    employed[i] ~ dnorm(mu[i], tau)
    # Calculate linear predictor
    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=modfile)

3. Initial values

Initial values can be specified as a list of lists, with one list element per MCMC chain. Each list element should itself be a named list corresponding to the values we want each parameter initialized at. We don’t necessarily need to explicitly initialize every parameter. We can also just set inits = NULL to allow JAGS to do the initialization automatically, but this will not work for some complex models. We can also provide a function which generates a list of initial values, which jagsUI will execute for each MCMC chain. This is what we’ll do below.

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

4. Parameters to monitor

Next, we choose which parameters from the model file we want to save posterior distributions for. We’ll save the parameters for the intercept (alpha), slope (beta), and residual standard deviation (sigma).

params <- c('alpha','beta','sigma')

5. MCMC settings

We’ll run 3 MCMC chains (n.chains = 3).

JAGS will start each chain by running adaptive iterations, which are used to tune and optimize MCMC performance. We will manually specify the number of adaptive iterations (n.adapt = 100). You can also try n.adapt = NULL, which will keep running adaptation iterations until JAGS reports adaptation is sufficient. In general you do not want to skip adaptation.

Next we need to specify how many regular iterations to run in each chain in total. We’ll set this to 1000 (n.iter = 1000). We’ll specify the number of burn-in iterations at 500 (n.burnin = 500). Burn-in iterations are discarded, so here we’ll end up with 500 iterations per chain (1000 total - 500 burn-in). We can also set the thinning rate: with n.thin = 2 we’ll keep only every 2nd iteration. Thus in total we will have 250 iterations saved per chain ((1000 - 500) / 2).

The optimal MCMC settings will depend on your specific dataset and model.

6. Run JAGS

We’re finally ready to run JAGS, via the jags function. We provide our data to the data argument, initial values function to inits, our vector of saved parameters to parameters.to.save, and our model file path to model.file. After that we specify the MCMC settings described above.

out <- jags(data = jags_data,
            inits = inits,
            parameters.to.save = params,
            model.file = modfile,
            n.chains = 3,
            n.adapt = 100,
            n.iter = 1000,
            n.burnin = 500,
            n.thin = 2)
# 
# Processing function input....... 
# 
# Done. 
#  
# Compiling model graph
#    Resolving undeclared variables
#    Allocating nodes
# Graph information:
#    Observed stochastic nodes: 16
#    Unobserved stochastic nodes: 3
#    Total graph size: 74
# 
# Initializing model
# 
# Adaptive phase, 100 iterations x 3 chains 
# If no progress bar appears JAGS has decided not to adapt 
#  
# 
#  Burn-in phase, 500 iterations x 3 chains 
#  
# 
# Sampling from joint posterior, 500 iterations x 3 chains 
#  
# 
# Calculating statistics....... 
# 
# Done.

We should see information and progress bars in the console.

If we have a long-running model and a powerful computer, we can tell jagsUI to run each chain on a separate core in parallel by setting argument parallel = TRUE:

out <- jags(data = jags_data,
            inits = inits,
            parameters.to.save = params,
            model.file = modfile,
            n.chains = 3,
            n.adapt = 100,
            n.iter = 1000,
            n.burnin = 500,
            n.thin = 2,
            parallel = TRUE)

While this is usually faster, we won’t be able to see progress bars when JAGS runs in parallel.

7. Examine output

Our first step is to look at the output object out:

out
# JAGS output for model '/tmp/RtmpqDC4Go/file4e463152be9', generated by jagsUI.
# Estimates based on 3 chains of 1000 iterations,
# adaptation = 100 iterations (sufficient),
# burn-in = 500 iterations and thin rate = 2,
# yielding 750 total samples from the joint posterior. 
# MCMC ran for 0.001 minutes at time 2026-09-18 08:22:24.074078.
# 
#            mean    sd   2.5%    50%  97.5% overlap0 f  Rhat n.eff
# alpha    51.853 0.832 50.160 51.849 53.568    FALSE 1 1.004   750
# beta      0.035 0.002  0.031  0.035  0.039    FALSE 1 1.002   750
# sigma     0.732 0.170  0.496  0.705  1.117    FALSE 1 1.015   249
# deviance 33.593 3.308 30.005 32.586 41.894    FALSE 1 1.022   281
# 
# Successful convergence based on Rhat values (all < 1.1). 
# Rhat is the potential scale reduction factor (at convergence, Rhat=1). 
# For each parameter, n.eff is a crude measure of effective sample size. 
# 
# overlap0 checks if 0 falls in the parameter's 95% credible interval.
# f is the proportion of the posterior with the same sign as the mean;
# i.e., our confidence that the parameter is positive or negative.
# 
# DIC info: (pD = var(deviance)/2) 
# pD = 5.4 and DIC = 39.039 
# DIC is an estimate of expected predictive error (lower is better).

We first get some information about the MCMC run. Next we see a table of summary statistics for each saved parameter, including the mean, median, and 95% credible intervals. The overlap0 column indicates if the 95% credible interval overlaps 0, and the f column is the proportion of posterior samples with the same sign as the mean.

The out object is a list with many components:

names(out)
#  [1] "sims.list"   "mean"        "sd"          "q2.5"        "q25"        
#  [6] "q50"         "q75"         "q97.5"       "overlap0"    "f"          
# [11] "Rhat"        "n.eff"       "pD"          "DIC"         "summary"    
# [16] "samples"     "modfile"     "model"       "parameters"  "mcmc.info"  
# [21] "run.date"    "parallel"    "bugs.format" "calc.DIC"

We’ll describe some of these below.

Diagnostics

We should pay special attention to the Rhat and n.eff columns in the output summary, which are MCMC diagnostics. The Rhat (Gelman-Rubin diagnostic) values for each parameter should be close to 1 (typically, < 1.1) if the chains have converged for that parameter. The n.eff value is the effective MCMC sample size and should ideally be close to the number of saved iterations across all chains (here 750, 3 chains * 250 samples per chain). In this case, both diagnostics look good.

We can also visually assess convergence using the traceplot function:

traceplot(out)

We should see the lines for each chain overlapping and not trending up or down.

Posteriors

We can quickly visualize the posterior distributions of each parameter using the densityplot function:

densityplot(out)

The traceplots and posteriors can be plotted together using plot:

plot(out)

We can also generate a posterior plot manually. To do this we’ll need to extract the actual posterior samples for a parameter. These are contained in the sims.list element of out.

post_alpha <- out$sims.list$alpha
hist(post_alpha, xlab="Value", main = "alpha posterior")

Update

If we need more iterations or want to save different parameters, we can use update:

# Now save mu also
params <- c(params, "mu")
out2 <- update(out, n.iter=300, parameters.to.save = params)
# Compiling model graph
#    Resolving undeclared variables
#    Allocating nodes
# Graph information:
#    Observed stochastic nodes: 16
#    Unobserved stochastic nodes: 3
#    Total graph size: 74
# 
# Initializing model
# 
# Adaptive phase..... 
# Adaptive phase complete 
#  
# No burn-in specified 
#  
# Sampling from joint posterior, 300 iterations x 3 chains 
#  
# 
# Calculating statistics....... 
# 
# Done.

The mu parameter is now in the output:

out2
# JAGS output for model '/tmp/RtmpqDC4Go/file4e463152be9', generated by jagsUI.
# Estimates based on 3 chains of 1300 iterations,
# adaptation = 100 iterations (sufficient),
# burn-in = 1000 iterations and thin rate = 2,
# yielding 450 total samples from the joint posterior. 
# MCMC ran for 0 minutes at time 2026-09-18 08:22:25.185847.
# 
#            mean    sd   2.5%    50%  97.5% overlap0 f  Rhat n.eff
# alpha    51.861 0.782 50.417 51.876 53.439    FALSE 1 1.008   450
# beta      0.035 0.002  0.031  0.035  0.038    FALSE 1 1.005   450
# sigma     0.733 0.169  0.505  0.706  1.198    FALSE 1 1.019   172
# mu[1]    59.986 0.365 59.294 59.990 60.752    FALSE 1 1.010   450
# mu[2]    60.857 0.324 60.239 60.862 61.534    FALSE 1 1.010   450
# mu[3]    60.810 0.326 60.189 60.814 61.492    FALSE 1 1.010   450
# mu[4]    61.730 0.286 61.168 61.734 62.334    FALSE 1 1.010   450
# mu[5]    63.269 0.229 62.835 63.273 63.741    FALSE 1 1.007   450
# mu[6]    63.894 0.211 63.483 63.895 64.338    FALSE 1 1.004   450
# mu[7]    64.532 0.197 64.136 64.528 64.939    FALSE 1 1.002   450
# mu[8]    64.453 0.198 64.055 64.449 64.869    FALSE 1 1.002   450
# mu[9]    65.645 0.187 65.279 65.641 65.993    FALSE 1 0.998   450
# mu[10]   66.398 0.191 66.031 66.400 66.736    FALSE 1 0.998   450
# mu[11]   67.216 0.205 66.821 67.223 67.593    FALSE 1 0.999   450
# mu[12]   67.277 0.206 66.880 67.286 67.656    FALSE 1 0.999   450
# mu[13]   68.601 0.246 68.113 68.617 69.061    FALSE 1 1.000   450
# mu[14]   69.291 0.272 68.735 69.300 69.803    FALSE 1 1.000   450
# mu[15]   69.831 0.294 69.210 69.850 70.378    FALSE 1 1.000   450
# mu[16]   71.104 0.351 70.346 71.128 71.751    FALSE 1 1.000   450
# deviance 33.526 3.193 30.064 32.587 42.081    FALSE 1 1.015   366
# 
# Successful convergence based on Rhat values (all < 1.1). 
# Rhat is the potential scale reduction factor (at convergence, Rhat=1). 
# For each parameter, n.eff is a crude measure of effective sample size. 
# 
# overlap0 checks if 0 falls in the parameter's 95% credible interval.
# f is the proportion of the posterior with the same sign as the mean;
# i.e., our confidence that the parameter is positive or negative.
# 
# DIC info: (pD = var(deviance)/2) 
# pD = 5.1 and DIC = 38.617 
# DIC is an estimate of expected predictive error (lower is better).

This is a good opportunity to show the whiskerplot function, which plots the mean and 95% CI of parameters in the jagsUI output:

whiskerplot(out2, 'mu')