# Introduction to jagsUI

### 2026-01-08

# 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/RtmpX27jnY/file585c369da170', 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 2026-01-08 12:33:41.915696.
    #
    #            mean    sd   2.5%    50%  97.5% overlap0 f  Rhat n.eff
    # alpha    51.823 0.798 50.208 51.800 53.327    FALSE 1 1.000   750
    # beta      0.035 0.002  0.031  0.035  0.039    FALSE 1 1.001   750
    # sigma     0.727 0.165  0.493  0.694  1.109    FALSE 1 0.999   750
    # deviance 33.496 3.112 30.045 32.747 40.964    FALSE 1 1.006   750
    #
    # 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 = 4.8 and DIC = 38.345
    # 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/RtmpX27jnY/file585c369da170', 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 2026-01-08 12:33:43.025374.
    #
    #            mean    sd   2.5%    50%  97.5% overlap0 f  Rhat n.eff
    # alpha    51.840 0.772 50.356 51.838 53.337    FALSE 1 1.002   450
    # beta      0.035 0.002  0.031  0.035  0.039    FALSE 1 1.005   450
    # sigma     0.724 0.154  0.490  0.700  1.072    FALSE 1 1.044   126
    # mu[1]    59.989 0.360 59.281 59.998 60.702    FALSE 1 0.999   450
    # mu[2]    60.863 0.321 60.234 60.879 61.503    FALSE 1 0.999   450
    # mu[3]    60.815 0.323 60.182 60.832 61.459    FALSE 1 0.999   450
    # mu[4]    61.739 0.283 61.188 61.740 62.289    FALSE 1 0.998   450
    # mu[5]    63.282 0.227 62.845 63.275 63.738    FALSE 1 0.999   450
    # mu[6]    63.909 0.209 63.511 63.904 64.336    FALSE 1 1.000   450
    # mu[7]    64.548 0.196 64.190 64.550 64.939    FALSE 1 1.001   450
    # mu[8]    64.469 0.197 64.106 64.470 64.861    FALSE 1 1.001   450
    # mu[9]    65.664 0.186 65.307 65.663 66.021    FALSE 1 1.003   450
    # mu[10]   66.419 0.190 66.052 66.414 66.773    FALSE 1 1.004   450
    # mu[11]   67.240 0.204 66.844 67.228 67.639    FALSE 1 1.005   450
    # mu[12]   67.302 0.205 66.901 67.290 67.705    FALSE 1 1.005   450
    # mu[13]   68.629 0.244 68.125 68.631 69.144    FALSE 1 1.005   450
    # mu[14]   69.321 0.270 68.759 69.327 69.904    FALSE 1 1.006   450
    # mu[15]   69.862 0.292 69.255 69.864 70.505    FALSE 1 1.006   450
    # mu[16]   71.140 0.348 70.421 71.134 71.921    FALSE 1 1.007   450
    # deviance 33.347 2.818 29.962 32.665 40.490    FALSE 1 1.052    95
    #
    # 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 = 3.9 and DIC = 37.249
    # 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')