jagsUI: Run JAGS from R
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, or get the development version from Github:
devtools::install_github('kenkellner/jagsUI')You will also need to separately install JAGS, which you can download here.
Example
library(jagsUI)Format data:
jags_data <- list(
gnp = longley$GNP,
employed = longley$Employed,
n = nrow(longley)
)Write BUGS model file:
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:
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:
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:
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
rjagsR package. - Andrew Gelman, Sibylle Sturtz, Uwe Ligges, Yu-Sung Su, and Masanao Yajima, developers of the
R2WinBUGSandR2jagspackages 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.
