# [Manual]Using Jags and R2jags in R

Manual
Author

Jihong

Published

September 12, 2018

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.

## 1 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.

## 2 Installation

To run jags with R, There is an interface with R called rjags. 1. Download and install Jags based on your operating system. 2. 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.

## 3 JAGS Examples

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

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

### 3.1 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)

### 3.2 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

### 3.3 Diagnostics

print(bayes.mod.fit)
Inference for Bugs model at "/var/folders/9t/ryz7lf_s7ts720p_lwdttfhm0000gn/T//RtmpaqCD4J/modelc9a07e55a3b4.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).
plot(bayes.mod.fit) traceplot(bayes.mod.fit)    