## Inlining JAGS models in R scripts for rjags

JAGS (Just Another Gibbs Sampler) is a general purpose MCMC engine similar to WinBUGS and OpenBUGS. I have a slight preference for JAGS as it is free and portable, works well on Linux, and interfaces well with R. It is tempting to write a tutorial introduction to JAGS and the corresponding R package, rjags, but there is a lot of material freely available on-line already, so it isn’t really necessary. If you are new to JAGS, I suggest starting with Getting Started with JAGS, rjags, and Bayesian Modelling. In this post I want to focus specifically on the problem of inlining JAGS models in R scripts as it can be very useful, and is usually skipped in introductory material.

#### JAGS and rjags on Ubuntu Linux

On recent versions of Ubuntu, assuming that R is already installed, the simplest way to install JAGS and rjags is using the command

sudo apt-get install jags r-cran-rjags


Now rjags is a CRAN package, so it can be installed in the usual way with install.packages("rjags"). However, taking JAGS and rjags direct from the Ubuntu repos should help to ensure that the versions of JAGS and rjags are in sync, which is a good thing.

#### Toy model

For this post, I will use a trivial toy example of inference for the mean and precision of a normal random sample. That is, we will assume data

$X_i \sim N(\mu,1/\tau),\quad i=1,2,\ldots n,$

with priors on $\mu$ and $\tau$ of the form

$\tau\sim Ga(a,b),\quad \mu \sim N(c,1/d).$

#### Separate model file

The usual way to fit this model in R using rjags is to first create a separate file containing the model

  model {
for (i in 1:n) {
x[i]~dnorm(mu,tau)
}
mu~dnorm(cc,d)
tau~dgamma(a,b)
}


Then, supposing that this file is called jags1.jags, an R session to fit the model could be constructed as follows:

require(rjags)
x=rnorm(15,25,2)
data=list(x=x,n=length(x))
hyper=list(a=3,b=11,cc=10,d=1/100)
init=list(mu=0,tau=1)
model=jags.model("jags1.jags",data=append(data,hyper), inits=init)
update(model,n.iter=100)
output=coda.samples(model=model,variable.names=c("mu", "tau"), n.iter=10000, thin=1)
print(summary(output))
plot(output)


This is all fine, and it can be very useful to have the model declared in a separate file, especially if the model is large and complex, and you might want to use it from outside R. However, very often for simple models it can be quite inconvenient to have the model separate from the R script which runs it. In particular, people often have issues with naming files correctly, making sure R is looking in the correct directory, moving the model with the R script, etc. So it would be nice to be able to just inline the JAGS model within an R script, to keep the model, the data, and the analysis all together in one place.

#### Using a temporary file

What we want to do is declare the JAGS model within a text string inside an R script and then somehow pass this into the call to jags.model(). The obvious way to do this is to write the string to a text file, and then pass the name of that text file into jags.model(). This works fine, but some care needs to be taken to make sure this works in a generic platform independent way. For example, you need to write to a file that you know doesn’t exist in a directory that is writable using a filename that is valid on the OS on which the script is being run. For this purpose R has an excellent little function called tempfile() which solves exactly this naming problem. It should always return the name of a file which does not exist in a writable directly within the standard temporary file location on the OS on which R is being run. This function is exceedingly useful for all kinds of things, but doesn’t seem to be very well known by newcomers to R. Using this we can construct a stand-alone R script to fit the model as follows:

require(rjags)
x=rnorm(15,25,2)
data=list(x=x,n=length(x))
hyper=list(a=3,b=11,cc=10,d=1/100)
init=list(mu=0,tau=1)
modelstring="
model {
for (i in 1:n) {
x[i]~dnorm(mu,tau)
}
mu~dnorm(cc,d)
tau~dgamma(a,b)
}
"
tmpf=tempfile()
tmps=file(tmpf,"w")
cat(modelstring,file=tmps)
close(tmps)
model=jags.model(tmpf,data=append(data,hyper), inits=init)
update(model,n.iter=100)
output=coda.samples(model=model,variable.names=c("mu", "tau"), n.iter=10000, thin=1)
print(summary(output))
plot(output)


Now, although there is a file containing the model temporarily involved, the script is stand-alone and portable.

#### Using a text connection

The solution above works fine, but still involves writing a file to disk and reading it back in again, which is a bit pointless in this case. We can solve this by using another under-appreciated R function, textConnection(). Many R functions which take a file as an argument will work fine if instead passed a textConnection object, and the rjags function jags.model() is no exception. Here, instead of writing the model string to disk, we can turn it into a textConnection object and then pass that directly into jags.model() without ever actually writing the model file to disk. This is faster, neater and cleaner. An R session which takes this approach is given below.

require(rjags)
x=rnorm(15,25,2)
data=list(x=x,n=length(x))
hyper=list(a=3,b=11,cc=10,d=1/100)
init=list(mu=0,tau=1)
modelstring="
model {
for (i in 1:n) {
x[i]~dnorm(mu,tau)
}
mu~dnorm(cc,d)
tau~dgamma(a,b)
}
"
model=jags.model(textConnection(modelstring), data=append(data,hyper), inits=init)
update(model,n.iter=100)
output=coda.samples(model=model,variable.names=c("mu", "tau"), n.iter=10000, thin=1)
print(summary(output))
plot(output)


This is my preferred way to use rjags. Note again that textConnection objects have many and varied uses and applications that have nothing to do with rjags.

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### 2 Responses to “Inlining JAGS models in R scripts for rjags”

1. Ben Bolker Says:

An even nicer way (in my opinion) is to copy the write.model() function from R2WinBUGS …

2. Getting started with Bayesian variable selection using JAGS and rjags « Darren Wilkinson's research blog Says:

[...] a previous post I gave a quick introduction to using the rjags R package to access the JAGS Bayesian inference from [...]