R Under development (unstable) (2024-03-09 r86082 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> # Requires rjags:
> if(require('rjags')){
+ 
+ 
+ 	library('runjags')
+ 	runjags.options(inits.warning=FALSE, rng.warning=FALSE, blockignore.warning=FALSE)
+ 
+ 	library('parallel')
+ 
+ 	testnum <- 1
+ 
+ 	themodel <- "
+ 	model{
+ 
+ 		for(i in 1:N){
+ 			Y[i] ~ dnorm(true.y[i], precision)
+ 			true.y[i] <- (m * X[i]) + c
+ 		}
+ 		m ~ dunif(-1000,1000)
+ 		c ~ dunif(-1000,1000)
+ 		precision ~ dexp(1)
+ 
+ 		#data# N, X
+ 	}"
+ 
+ 	# Simulate the data
+ 	set.seed(1)
+ 	N <- 20
+ 	X <- 1:N
+ 	Y <- rnorm(length(X), 2*X + 1, 1)
+ 
+ 	# Some initial values to use for 2 chains:
+ 
+ 	initfun <- function(chain){
+ 
+ 		# data is made available within this function when it
+ 		# is evaluated for each simulation:
+ 		stopifnot(length(data$X) == data$N)
+ 
+ 		m <- c(-10,10)[chain]
+ 		c <- c(10,-10)[chain]
+ 		precision <- c(0.01,100)[chain]
+ 
+ 		.RNG.seed <- chain
+ 		.RNG.name <- c("base::Super-Duper",
+ 		"base::Wichmann-Hill")[chain]
+ 
+ 		return(list(m=m, c=c, precision=precision,
+ 		.RNG.seed=.RNG.seed, .RNG.name=.RNG.name))
+ 	}
+ 
+ 	# A simple function that removes (over-writes with NA) one datapoint at a time:
+ 	datafun <- function(s){
+ 		simdata <- Y
+ 		simdata[s] <- NA
+ 		return(list(Y=simdata))
+ 	}
+ 
+ 
+ 	# Set up a cluster to use with the parLapply method:
+ 	cat('Running study test number', testnum, '\n'); testnum <- testnum+1
+ 	cl <- makeCluster(2)
+ 	# Call the simulations over the snow cluster:
+ 	results <- run.jags.study(simulations=4, model=themodel, datafunction=datafun,
+ 	targets=list(Y=Y, m=2, c=1), n.chains=2, inits=initfun, cl=cl)
+ 
+ 
+ 	m <- 'model{
+ 	d[1] ~ dpois(mu)
+ 	d[2] ~ dpois(mu)
+ 	d[3] ~ dpois(mu)
+ 	mu ~ dgamma(1,1)
+ 	#monitor# mu
+ 	#data# d
+ 	}'
+ 
+ 	##### Can't test any more than 2 spawned processes on winbuilder #####
+ 
+ 	cat('Running study test number', testnum, '\n'); testnum <- testnum+1
+ 	mu <- list(1,1)
+ 	d <- c(5, 4, 7)
+ 	jr <- run.jags(m, method='rjags', n.chains=2, inits=list(list(mu=1), list(mu=1)), silent.jags=TRUE)
+ 	# Drop 1 (would create 3 clusters except we pass it cl):
+ 	r <- drop.k(jr, dropvars='d', cl=cl)
+ 	stopCluster(cl)
+ 
+ 	# Drop k (use lapply so we don't create a cluster with 4 nodes):
+ 	cat('Running study test number', testnum, '\n'); testnum <- testnum+1
+ 	r <- drop.k(jr, dropvars='d', simulations=4, k=2, silent.jags=TRUE, parallel.method=lapply)
+ 	
+ 	cat("All study/drop-k checks passed\n")
+ 	
+ }else{
+ 	cat("Note: the rjags package is not installed, so the study/drop-k tests were skipped\n")
+ }
Loading required package: rjags
Loading required package: coda
Linked to JAGS 4.3.1
Loaded modules: basemod,bugs
Running study test number 1 

Starting a JAGS study at 17:54
Testing the model and data for simulation 2...
Finished running the simulation
The model runs OK
Calling autorun.jags for 4 simulations...
Using a PSOCK cluster with 2 nodes on host 'localhost'
Finished running the simulations
Finished summarising results
Finished JAGS study at 17:54 (total time taken: 11.5 seconds)

Running study test number 2 
Finished running the simulation

Starting a JAGS study at 17:54
Checking the supplied model definition (and data for simulation 3)...
The model compiles OK
Calling autorun.jags for 3 simulations...
Using a PSOCK cluster with 2 nodes on host 'localhost'
Finished running the simulations
Finished summarising results
Finished JAGS study at 17:54 (total time taken: 5.8 seconds)

Running study test number 3 

Starting a JAGS study at 17:54
Checking the supplied model definition (and data for simulation 3)...
The model compiles OK
Calling autorun.jags for 4 simulations...
Finished running the simulations
Finished summarising results
Finished JAGS study at 17:54 (total time taken: 10.9 seconds)

All study/drop-k checks passed
Warning message:
One or more target variables is non-stochastic for all of the (successful) simulation results 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   3.89    0.67   31.37