# Tricks for working with JAGS

**Posted:**May 8, 2015

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**Tags:**BUGS, JAGS, R Leave a comment

- The Gamma distribution is frequently used as a prior for the precision in a model because of its conjugacy with the Normal. Given JAGS parameterization of the Gamma distribution with a rate and shape parameter, it’s not easy to select sensible hyper-parameters. It’s easier to think of hyper-parameters that correspond to the mean and the variance of a prior for the precision. To use mean and variance hyper-parameters (mu and sigma) with JAGS’s
`dgamma`

, use:`dgamma( ((mu^2)/sigma), (mu/sigma))`

. - The
`glm`

module provides a series of more efficient samplers that JAGS automatically selects whenever possible (e.g. Albert/Chib (1993)). This usually leads to much better mixing. I tend to load the module right away with the actual`rjags`

package, e.i.`library(rjags); load.module("glm")`

. - To check which samplers are selected by JAGS, use the
`list.samplers()`

function on the JAGS model object. This is useful, because sometimes a slight change in the coding helps JAGS to select better samplers. For example, when using a logit model in JAGS and you want the Holmes-Held sampler from the glm module to work, do not use`dmnorm()`

but`dnorm()`

for the prior of the coefficient, e.i. do not use blocking. - Avoid deterministic nodes as much as possible to increase speed. In particular, do all pre- and post processing in R including drawing from the predictive distribution and compute deterministic functions inside probabilistic statements, e.g.
`y ~ dnorm( (x*beta), 1)`

instead of`mu .`

- For comprehensible / readable code, let the running index be the small letter corresponding to the capital letter of the upper limit, e.i.
`for(n in 1:N)`

instead of`for(i in 1:N)`

. This becomes especially useful when one has many nested hierarchies.

# Gibbs Samplers in R and Notes

**Posted:**May 4, 2015

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**Tags:**MCMC, R Leave a comment

I uploaded several `R`

files and notes for Gibbs samplers to my Website’s resource page. They include: Factor Analysis, Finite Mixture of Linear Regression Models and Probit Regression. I have written all this a while ago when I was reading about these models. Most of the samplers are fairly standard and well-implemented efficiently in `R`

packages calling `c++`

in the background. These packages are also referenced in the `R`

files. Nevertheless, the `R`

code might be useful for pedagogical purposes or as building block for more complex schemes.