• using R version 4.5.3 (2026-03-11)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-15 (Debian 15.2.0-16) 15.2.0     GNU Fortran (Debian 15.2.0-16) 15.2.0
• running under: Debian GNU/Linux forky/sid
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• this is package ‘R2jags’ version ‘0.8-9’
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• checking examples ... [2s/3s] ERROR Running examples in ‘R2jags-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: jags > ### Title: Run 'JAGS' from R > ### Aliases: rjags-class rjags.parallel-class jags jags2 jags.parallel > ### Keywords: interface models > > ### ** Examples > > # 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) # Bugs model file for 8 schools analysis from Section 5.5 of "Bayesian Data # Analysis". Save this into the file "schools.bug" in your R working directory. model {   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 } > # 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) module glm loaded Compiling model graph    Resolving undeclared variables    Allocating nodes Graph information:    Observed stochastic nodes: 8    Unobserved stochastic nodes: 10    Total graph size: 41 Initializing model > > ## 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) Compiling model graph    Resolving undeclared variables    Allocating nodes Graph information:    Observed stochastic nodes: 8    Unobserved stochastic nodes: 10    Total graph size: 41 Initializing model > > ## 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) Compiling model graph    Resolving undeclared variables    Allocating nodes Graph information:    Observed stochastic nodes: 8    Unobserved stochastic nodes: 10    Total graph size: 41 Initializing model > > ## 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) Compiling model graph    Resolving undeclared variables    Allocating nodes Graph information:    Observed stochastic nodes: 8    Unobserved stochastic nodes: 10    Total graph size: 41 Initializing model > 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) Compiling model graph    Resolving undeclared variables    Allocating nodes Graph information:    Observed stochastic nodes: 8    Unobserved stochastic nodes: 10    Total graph size: 41 Initializing model > > > #===============================# > # RUN jags and postprocessing # > #===============================# > jagsfit <- jags(data=jags.data, inits=jags.inits, jags.params, + n.iter=5000, model.file=model.file) Compiling model graph    Resolving undeclared variables    Allocating nodes Graph information:    Observed stochastic nodes: 8    Unobserved stochastic nodes: 10    Total graph size: 41 Initializing model > > # 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) Compiling model graph    Resolving undeclared variables    Allocating nodes Graph information:    Observed stochastic nodes: 8    Unobserved stochastic nodes: 10    Total graph size: 41 Initializing model > > # 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) Error in serverSocket(port = port) :   creation of server socket failed: port 11390 cannot be opened Calls: jags.parallel -> makeCluster -> makePSOCKcluster -> serverSocket Execution halted
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• DONE

Status: 1 ERROR