[Manual]Using Jags and R2jags in R

Last updated on May 2, 2022 5 min read Manual

This post is aimed to introduce the basics of using jags in R programming. Jags is a frequently used program for conducting Bayesian statistics.Most of information below is borrowed from Jeromy Anglim’s Blog. I will keep editing this post if I found more resources about jags.

What is JAGS?

JAGS stands for Just Another Gibbs Sampler. To quote the program author, Martyn Plummer, “It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation…” It uses a dialect of the BUGS language, similar but a little different to OpenBUGS and WinBUGS.

Installation

To run jags with R, There is an interface with R called rjags.

Download and install Jags based on your operating system.
Install additional R packages: type install.packages(c(“rjags”,”coda”)) in R console. rjags is to interface with JAGS and coda is to process MCMC output.

JAGS Examples

There are a lot of examples online. The following provides links or simple codes to JAGS code.

Justin Esarey
- An entire course on Bayesian Statistics with examples in R and JAGS. It includes 10 lectures and each lecture lasts around 2 hours. The content is designed for a social science audience and it includes a syllabus linking with Simon Jackman’s text. The videos are linked from above or available direclty on YouTube.
Jeromy Anglim
- The author of this blog also provides a few examples. He shared the codes on his github account
John Myles White
- A course on statistical models that is under development with JAGS scripts on github.
- Simple introductory examples of fitting a normal distribution, linear regression, and logistic regression
- A follow-up post demonstrating the use of the coda package with rjags to perform MCMC diagnostics.
A simple simulation sample:

First, simulate the Data:

library(R2jags)
n.sim <- 100; set.seed(123)
x1 <- rnorm(n.sim, mean = 5, sd = 2)
x2 <- rbinom(n.sim, size = 1, prob = 0.3)
e <- rnorm(n.sim, mean = 0, sd = 1)

Next, we create the outcome y based on coefficients \(b_1\) and \(b_2\) for the respective predictors and an intercept a:

b1 <- 1.2
b2 <- -3.1
a <- 1.5
y <- b1*x1 + b2*x2 + e

Now, we combine the variables into one dataframe for processing later:

sim.dat <- data.frame(y, x1, x2)

And we create and summarize a (frequentist) linear model fit on these data:

freq.mod <- lm(y ~ x1 + x2, data = sim.dat)
summary(freq.mod)

## 
## Call:
## lm(formula = y ~ x1 + x2, data = sim.dat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3432 -0.6797 -0.1112  0.5367  3.2304 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.34949    0.28810   1.213    0.228    
## x1           1.13511    0.05158  22.005   <2e-16 ***
## x2          -3.09361    0.20650 -14.981   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9367 on 97 degrees of freedom
## Multiple R-squared:  0.8772,	Adjusted R-squared:  0.8747 
## F-statistic: 346.5 on 2 and 97 DF,  p-value: < 2.2e-16

Beyesian Model

bayes.mod <- function() {
 for(i in 1:N){
 y[i] ~ dnorm(mu[i], tau)
 mu[i] <- alpha + beta1 * x1[i] + beta2 * x2[i]
 }
 alpha ~ dnorm(0, .01)
 beta1 ~ dunif(-100, 100)
 beta2 ~ dunif(-100, 100)
 tau ~ dgamma(.01, .01)
}

Now define the vectors of the data matrix for JAGS:

y <- sim.dat$y
x1 <- sim.dat$x1
x2 <- sim.dat$x2
N <- nrow(sim.dat)

Read in the data frame for JAGS

sim.dat.jags <- list("y", "x1", "x2", "N")

Define the parameters whose posterior distributions you are interested in summarizing later:

bayes.mod.params <- c("alpha", "beta1", "beta2")

Setting up starting values

bayes.mod.inits <- function(){
 list("alpha" = rnorm(1), "beta1" = rnorm(1), "beta2" = rnorm(1))
}

# inits1 <- list("alpha" = 0, "beta1" = 0, "beta2" = 0)
# inits2 <- list("alpha" = 1, "beta1" = 1, "beta2" = 1)
# inits3 <- list("alpha" = -1, "beta1" = -1, "beta2" = -1)
# bayes.mod.inits <- list(inits1, inits2, inits3)

Fitting the model

set.seed(123)
bayes.mod.fit <- jags(data = sim.dat.jags, inits = bayes.mod.inits,
  parameters.to.save = bayes.mod.params, n.chains = 3, n.iter = 9000,
  n.burnin = 1000, model.file = bayes.mod)

## module glm loaded

## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 100
##    Unobserved stochastic nodes: 4
##    Total graph size: 511
## 
## Initializing model

Diagnostics

print(bayes.mod.fit)

## Inference for Bugs model at "/var/folders/k4/t941vy1d41dgft9y4hf623b00000gp/T//RtmpZJgW31/model8f09b1f5142.txt", fit using jags,
##  3 chains, each with 9000 iterations (first 1000 discarded), n.thin = 8
##  n.sims = 3000 iterations saved
##          mu.vect sd.vect    2.5%     25%     50%     75%   97.5%  Rhat n.eff
## alpha      0.362   0.293  -0.205   0.166   0.358   0.562   0.958 1.009   250
## beta1      1.133   0.053   1.025   1.099   1.134   1.169   1.236 1.009   250
## beta2     -3.090   0.205  -3.496  -3.231  -3.090  -2.950  -2.685 1.002  1700
## deviance 271.830   2.899 268.167 269.718 271.122 273.198 279.223 1.000  3000
## 
## For each parameter, n.eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
## 
## DIC info (using the rule, pD = var(deviance)/2)
## pD = 4.2 and DIC = 276.0
## DIC is an estimate of expected predictive error (lower deviance is better).