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