# Run JAGS from R The `jags` function takes data and starting values as input. It automatically writes a `jags` script, calls the model, and saves the simulations for easy access in R. class ```r jags(data, inits, parameters.to.save, model.file="model.bug", n.chains=3, n.iter=2000, n.burnin=floor(n.iter/2), n.thin=max(1, floor((n.iter - n.burnin) / 1000)), DIC=TRUE, pD = FALSE, n.iter.pd = NULL, n.adapt = 100, working.directory=NULL, jags.seed = 123, refresh = n.iter/50, progress.bar = "text", digits=5, RNGname = c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister"), jags.module = c("glm","dic"), quiet = FALSE, checkMissing = FALSE ) jags.parallel(data, inits, parameters.to.save, model.file = "model.bug", n.chains = 2, n.iter = 2000, n.burnin = floor(n.iter/2), n.thin = max(1, floor((n.iter - n.burnin)/1000)), n.cluster= n.chains, DIC = TRUE, working.directory = NULL, jags.seed = 123, digits=5, RNGname = c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister"), jags.module = c("glm","dic"), export_obj_names=NULL, envir = .GlobalEnv ) jags2(data, inits, parameters.to.save, model.file="model.bug", n.chains=3, n.iter=2000, n.burnin=floor(n.iter/2), n.thin=max(1, floor((n.iter - n.burnin) / 1000)), DIC=TRUE, jags.path="", working.directory=NULL, clearWD=TRUE, refresh = n.iter/50) ``` ## Arguments - `data`: (1) a vector or list of the names of the data objects used by the model, (2) a (named) list of the data objects themselves, or (3) the name of a "dump" format file containing the data objects, which must end in ".txt", see example below for details. - `inits`: a list with `n.chains` elements; each element of the list is itself a list of starting values for the `BUGS` model, **or** a function creating (possibly random) initial values. If inits is `NULL`, `JAGS` will generate initial values for parameters. - `parameters.to.save`: character vector of the names of the parameters to save which should be monitored. - `model.file`: file containing the model written in `BUGS` code. Alternatively, as in `R2WinBUGS`, `model.file` can be an R function that contains a `BUGS` model that is written to a temporary model file (see `tempfile`) using `write.model` - `n.chains`: number of Markov chains (default: 3) - `n.iter`: number of total iterations per chain (including burn in; default: 2000) - `n.burnin`: length of burn in, i.e. number of iterations to discard at the beginning. Default is `n.iter/2`, that is, discarding the first half of the simulations. If n.burnin is 0, `jags()` will run 100 iterations for adaption. - `n.cluster`: number of clusters to use to run parallel chains. Default equals n.chains. - `n.thin`: thinning rate. Must be a positive integer. Set `n.thin` > 1 to save memory and computation time if `n.iter` is large. Default is `max(1, floor(n.chains * (n.iter-n.burnin) / 1000))` which will only thin if there are at least 2000 simulations. - `DIC`: logical; if `TRUE` (default), compute deviance, pD, and DIC. The rule `pD=var(deviance) / 2` is used. - `pD`: logical; if `TRUE` and `DIC` is also `TRUE`, then adds the computation of 'pD', using 'rjags::dic.samples()'. Defaults to `FALSE`. - `n.iter.pd`: number of iterations to feed 'rjags::dic.samples()' to compute 'pD'. Defaults at 1000. - `n.adapt`: number of iterations for which to run the adaptation, when creating the model object. Defaults at 100. - `working.directory`: sets working directory during execution of this function; This should be the directory where model file is. - `jags.seed`: random seed for `JAGS`, default is 123. This function is used for jags.parallell() and does not work for jags(). Use set.seed() instead if you want to produce identical result with jags() . - `jags.path`: directory that contains the `JAGS` executable. The default is . - `clearWD`: indicating whether the files data.txt , inits[1:n.chains].txt , codaIndex.txt , jagsscript.txt , and CODAchain[1:nchains].txt should be removed after `jags` has finished, default=TRUE. - `refresh`: refresh frequency for progress bar, default is `n.iter/50` - `progress.bar`: type of progress bar. Possible values are text , gui , and none . Type text is displayed on the R console. Type gui is a graphical progress bar in a new window. The progress bar is suppressed if `progress.bar` is none - `digits`: as in `write.model` in the `R2WinBUGS` package: number of significant digits used for `BUGS` input, see `formatC`. Only used if specifying a `BUGS` model as an R function. - `RNGname`: the name for random number generator used in JAGS. There are four RNGS supplied by the base moduale in JAGS: `Wichmann-Hill`, `Marsaglia-Multicarry`, `Super-Duper`, `Mersenne-Twister` - `jags.module`: the vector of jags modules to be loaded. Default are glm and dic . Input NULL if you don't want to load any jags module. - `export_obj_names`: character vector of objects to export to the clusters. - `envir`: default is .GlobalEnv - `quiet`: Logical, whether to suppress stdout in `jags.model()`. - `checkMissing`: Default: FALSE. When TRUE, checks for missing data in categorical parameters and returns a `sim.list` with NA values if detected. It's recommended to supply `jags()` with complete data. ## Details To run: 1. Write a `JAGS` model in an ASCII file. 2. Go into . 3. Prepare the inputs for the `jags` function and run it (see Example section). 4. The model will now run in `JAGS`. It might take awhile. You will see things happening in the R console. ## Author(s) Yu-Sung Su suyusung@tsinghua.edu.cn , Masanao Yajima yajima@bu.edu , Gianluca Baio g.baio@ucl.ac.uk ## References Plummer, Martyn (2003) JAGS: A program for analysis of Bayesian graphical models using Gibbssampling. [https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf](https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf). Gelman, A., Carlin, J. B., Stern, H.S., Rubin, D.B. (2003) **Bayesian Data Analysis**, 2nd edition, CRC Press. Sibylle Sturtz and Uwe Ligges and Andrew Gelman. (2005). R2WinBUGS: A Package for Running WinBUGS from R. **Journal of Statistical Software** 3 (12): 1--6. ## Examples ```r # An example model file is given in: model.file <- system.file(package="R2jags", "model", "schools.txt") # Let's take a look: file.show(model.file) # you can also write BUGS model as a R function, see below: #=================# # initialization # #=================# # data J <- 8.0 y <- c(28.4,7.9,-2.8,6.8,-0.6,0.6,18.0,12.2) sd <- c(14.9,10.2,16.3,11.0,9.4,11.4,10.4,17.6) jags.data <- list("y","sd","J") jags.params <- c("mu","sigma","theta") jags.inits <- function(){ list("mu"=rnorm(1),"sigma"=runif(1),"theta"=rnorm(J)) } ## You can input data in 4 ways ## 1) data as list of character jagsfit <- jags(data=list("y","sd","J"), inits=jags.inits, jags.params, n.iter=10, model.file=model.file) ## 2) data as character vector of names jagsfit <- jags(data=c("y","sd","J"), inits=jags.inits, jags.params, n.iter=10, model.file=model.file) ## 3) data as named list jagsfit <- jags(data=list(y=y,sd=sd,J=J), inits=jags.inits, jags.params, n.iter=10, model.file=model.file) ## 4) data as a file fn <- "tmpbugsdata.txt" dump(c("y","sd","J"), file=fn) jagsfit <- jags(data=fn, inits=jags.inits, jags.params, n.iter=10, model.file=model.file) unlink("tmpbugsdata.txt") ## You can write bugs model in R as a function schoolsmodel <- function() { for (j in 1:J){ # J=8, the number of schools y[j] ~ dnorm (theta[j], tau.y[j]) # data model: the likelihood tau.y[j] <- pow(sd[j], -2) # tau = 1/sigma^2 } for (j in 1:J){ theta[j] ~ dnorm (mu, tau) # hierarchical model for theta } tau <- pow(sigma, -2) # tau = 1/sigma^2 mu ~ dnorm (0.0, 1.0E-6) # noninformative prior on mu sigma ~ dunif (0, 1000) # noninformative prior on sigma } jagsfit <- jags(data=jags.data, inits=jags.inits, jags.params, n.iter=10, model.file=schoolsmodel) #===============================# # RUN jags and postprocessing # #===============================# jagsfit <- jags(data=jags.data, inits=jags.inits, jags.params, n.iter=5000, model.file=model.file) # Can also compute the DIC using pD (=Dbar-Dhat), via dic.samples(), which # is a closer approximation to the original formulation of Spiegelhalter et # al (2002), instead of pV (=var(deviance)/2), which is the default in JAGS jagsfit.pD <- jags(data=jags.data, inits=jags.inits, jags.params, n.iter=5000, model.file=model.file, pD=TRUE) # Run jags parallely, no progress bar. R may be frozen for a while, # Be patient. Currenlty update afterward does not run parallelly # jagsfit.p <- jags.parallel(data=jags.data, inits=jags.inits, jags.params, n.iter=5000, model.file=model.file) # display the output print(jagsfit) plot(jagsfit) # traceplot traceplot(jagsfit.p) traceplot(jagsfit) # or to use some plots in coda # use as.mcmmc to convert rjags object into mcmc.list jagsfit.mcmc <- as.mcmc(jagsfit.p) jagsfit.mcmc <- as.mcmc(jagsfit) ## now we can use the plotting methods from coda #require(lattice) #xyplot(jagsfit.mcmc) #densityplot(jagsfit.mcmc) # if the model does not converge, update it! jagsfit.upd <- update(jagsfit, n.iter=100) print(jagsfit.upd) print(jagsfit.upd, intervals=c(0.025, 0.5, 0.975)) plot(jagsfit.upd) # before update parallel jags object, do recompile it recompile(jagsfit.p) jagsfit.upd <- update(jagsfit.p, n.iter=100) # or auto update it until it converges! see ?autojags for details # recompile(jagsfit.p) jagsfit.upd <- autojags(jagsfit.p) jagsfit.upd <- autojags(jagsfit) # to get DIC or specify DIC=TRUE in jags() or do the following# dic.samples(jagsfit.upd$model, n.iter=1000, type="pD") # attach jags object into search path see "attach.bugs" for details attach.jags(jagsfit.upd) # this will show a 3-way array of the bugs.sim object, for example: mu # detach jags object into search path see "attach.bugs" for details detach.jags() # to pick up the last save session # for example, load("RWorkspace.Rdata") recompile(jagsfit) jagsfit.upd <- update(jagsfit, n.iter=100) recompile(jagsfit.p) jagsfit.upd <- update(jagsfit, n.iter=100) #=============# # using jags2 # #=============# ## jags can be run and produces coda files, but cannot be updated once it's done ## You may need to edit "jags.path" to make this work, ## also you need a write access in the working directory: ## e.g. setwd("d:/") ## NOT RUN HERE ## Not run: jagsfit <- jags2(data=jags.data, inits=jags.inits, jags.params, n.iter=5000, model.file=model.file) print(jagsfit) plot(jagsfit) # or to use some plots in coda # use as.mcmmc to convert rjags object into mcmc.list jagsfit.mcmc <- as.mcmc.list(jagsfit) traceplot(jagsfit.mcmc) #require(lattice) #xyplot(jagsfit.mcmc) #densityplot(jagsfit.mcmc) ## End(Not run) ```

jags function