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# jagsUI: Run JAGS from R
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[](https://cran.r-project.org/web/packages/jagsUI/index.html)
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This package runs `JAGS` (Just Another Gibbs Sampler) analyses from within `R`. It acts as a wrapper and alternative interface for the functions in the `rjags` package and adds some custom output and graphical options. It also makes running chains in parallel quick and easy.
## Installation
You can install the package from [CRAN](https://cran.r-project.org/web/packages/jagsUI/index.html), or get the development version from Github:
```r
devtools::install_github('kenkellner/jagsUI')
```
You will also need to separately install JAGS, which you can download [here](https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/).
## Example
```r
library(jagsUI)
```
Format data:
```r
jags_data <- list(
gnp = longley$GNP,
employed = longley$Employed,
n = nrow(longley)
)
```
Write BUGS model file:
```r
modfile <- tempfile()
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)
```
Set initial values and parameters to save:
```r
inits <- function(){
list(alpha=rnorm(1,0,1),
beta=rnorm(1,0,1),
sigma=runif(1,0,3)
)
}
params <- c('alpha','beta','sigma')
```
Run JAGS:
```r
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.
```
View output:
```r
out
```
```
## JAGS output for model '/tmp/Rtmp9aOSoz/file15fda23cf96c2', 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 2024-01-21 17:29:30.558591.
##
## mean sd 2.5% 50% 97.5% overlap0 f Rhat n.eff
## alpha 51.911 0.739 50.539 51.911 53.402 FALSE 1 0.999 750
## beta 0.035 0.002 0.031 0.035 0.038 FALSE 1 1.000 750
## sigma 0.715 0.148 0.487 0.699 1.095 FALSE 1 1.006 535
## deviance 33.247 2.798 30.050 32.466 40.263 FALSE 1 1.002 750
##
## 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 = 3.9 and DIC = 37.161
## DIC is an estimate of expected predictive error (lower is better).
```
## Acknowledgments
* Martyn Plummer, developer of the excellent JAGS software package and the `rjags` R package.
* Andrew Gelman, Sibylle Sturtz, Uwe Ligges, Yu-Sung Su, and Masanao Yajima, developers of the `R2WinBUGS` and `R2jags` packages on which the package was originally based.
* Robert Swihart, Marc Kery, Jerome Guelat, Michael Schaub, and Mike Meredith who tested and provided helpful suggestions and improvements for the package.
|
