R2WinBUGS (version 2.1-13)

bugs: Run WinBUGS and OpenBUGS from R or S-PLUS

Description

The bugs function takes data and starting values as input. It automatically writes a WinBUGS script, calls the model, and saves the simulations for easy access in Ror S-PLUS.

Usage

bugs(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.chains * (n.iter - n.burnin) / n.sims)),
    n.sims = 1000, bin=(n.iter - n.burnin) / n.thin,
    debug=FALSE, DIC=TRUE, digits=5, codaPkg=FALSE,
    bugs.directory="c:/Program Files/WinBUGS14/",
    program=c("WinBUGS", "OpenBUGS", "winbugs", "openbugs"),
    working.directory=NULL, clearWD=FALSE,
    useWINE=.Platform$OS.type != "windows", WINE=NULL,
    newWINE=TRUE, WINEPATH=NULL, bugs.seed=NULL, summary.only=FALSE,
    save.history=!summary.only)

Arguments

data

either a named list (names corresponding to variable names in the model.file) of the data for the WinBUGS model, or a vector or list of the names of the data objects used by the model. If data i

inits

a list with n.chains elements; each element of the list is itself a list of starting values for the WinBUGS model, or a function creating (possibly random) initial values. Alternatively, if inits=NULL<

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 WinBUGS code. The extension can be either .bug or .txt. If the extension is .bug and program=="WinBUGS", a copy of the file with extension <

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.

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

n.sims

The approximate number of simulations to keep after thinning.

bin

number of iterations between saving of results (i.e. the coda files are saved after each bin iterations); default is to save only at the end.

debug

if FALSE (default), WinBUGS is closed automatically when the script has finished running, otherwise WinBUGS remains open for further investigation

DIC

logical; if TRUE (default), compute deviance, pD, and DIC. This is done in WinBUGS directly using the rule pD = Dbar - Dhat. If there are less iterations than required for the adaptive phase, the rule <

digits

number of significant digits used for WinBUGS input, see formatC

codaPkg

logical; if FALSE (default) a bugs object is returned, if TRUE file names of WinBUGS output are returned for easy access by the coda package through function

bugs.directory

directory that contains the WinBUGS executable. If the global option R2WinBUGS.bugs.directory is not NULL, it will be used as the default.

program

the program to use, either winbugs/WinBUGS or openbugs/OpenBUGS, the latter makes use of function openbugs and requires the CRAN package <

working.directory

sets working directory during execution of this function; WinBUGS' in- and output will be stored in this directory; if NULL, a temporary working directory via tempdir

clearWD

logical; indicating whether the files data.txt, inits[1:n.chains].txt, log.odc, codaIndex.txt, and coda[1:nchains].txt should be removed after WinBUGS has finished.

useWINE

logical; attempt to use the Wine emulator to run WinBUGS, defaults to FALSE on Windows, and TRUE otherwise. Not available in S-PLUS.

WINE

character, path to wine binary file, it is tried hard (by a guess and the utilities which and locate) to get the information automatically if not given.

newWINE

Use new versions of Wine that have winepath utility

WINEPATH

character, path to winepath binary file, it is tried hard (by a guess and the utilities which and locate) to get the information automatically if not given.

bugs.seed

random seed for WinBUGS (default is no seed)

summary.only

If TRUE, only a parameter summary for very quick analyses is given, temporary created files are not removed in that case.

save.history

If TRUE (the default), trace plots are generated at the end.

Value

• If codaPkg=TRUE the returned values are the names of coda output files written by WinBUGS containing the Markov Chain Monte Carlo output in the CODA format. This is useful for direct access with read.bugs. If codaPkg=FALSE, the following values are returned:
• n.chains
  see Section Arguments
• n.iter
  see Section Arguments
• n.burnin
  see Section Arguments
• n.thin
  see Section Arguments
• n.keep
  number of iterations kept per chain (equal to (n.iter-n.burnin) / n.thin)
• n.sims
  number of posterior simulations (equal to n.chains * n.keep)
• sims.array
  3-way array of simulation output, with dimensions n.keep, n.chains, and length of combined parameter vector
• sims.list
  list of simulated parameters: for each scalar parameter, a vector of length n.sims for each vector parameter, a 2-way array of simulations, for each matrix parameter, a 3-way array of simulations, etc. (for convenience, the n.keep*n.chains simulations in sims.matrix and sims.list (but NOT sims.array) have been randomly permuted)
• sims.matrix
  matrix of simulation output, with n.chains*n.keep rows and one column for each element of each saved parameter (for convenience, the n.keep*n.chains simulations in sims.matrix and sims.list (but NOT sims.array) have been randomly permuted)
• summary
  summary statistics and convergence information for each saved parameter.
• mean
  a list of the estimated parameter means
• sd
  a list of the estimated parameter standard deviations
• median
  a list of the estimated parameter medians
• root.short
  names of argument parameters.to.save and deviance
• long.short
  indexes; programming stuff
• dimension.short
  dimension of indexes.short
• indexes.short
  indexes of root.short
• last.values
  list of simulations from the most recent iteration; they can be used as starting points if you wish to run WinBUGS for further iterations
• pD
  an estimate of the effective number of parameters, for calculations see the section Arguments.
• DIC
  mean(deviance) + pD

itemize

• MS Windows

item

Linux, Mac OS X and Unix in general

code

bugs.directory

pkg

WinBUGS

dQuote

• c:/Program Files/WinBUGS14/
• /path/to/wine/folder/dosdevices/c:/Program Files/WinBUGS14

Details

To run:

1. Write aBUGSmodel in an ASCII file (hint: usewrite.model).
2. Go intoR/ S-PLUS.
3. Prepare the inputs for thebugsfunction and run it (see Example section).
4. AWinBUGSwindow will pop up andR/ S-PLUS will freeze up. The model will now run inWinBUGS. It might take awhile. You will see things happening in the Log window withinWinBUGS. WhenWinBUGSis done, its window will close andR/ S-PLUS will work again.
5. If an error message appears, re-run withdebug=TRUE.

BUGS version support:

• WinBUGS1.4.*

{default} OpenBUGS 2.*{via argument program="OpenBUGS"}

References

Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B. (2003): Bayesian Data Analysis, 2nd edition, CRC Press. Sturtz, S., Ligges, U., Gelman, A. (2005): R2WinBUGS: A Package for Running WinBUGS from R. Journal of Statistical Software 12(3), 1-16.

See Also

print.bugs, plot.bugs, as well as coda and BRugs packages

Examples

# An example model file is given in:
model.file <- system.file(package="R2WinBUGS", "model", "schools.txt")
# Let's take a look:
file.show(model.file)

# Some example data (see ?schools for details):
data(schools)
schools

J <- nrow(schools)
y <- schools$estimate
sigma.y <- schools$sd
data <- list ("J", "y", "sigma.y")
inits <- function(){
    list(theta=rnorm(J, 0, 100), mu.theta=rnorm(1, 0, 100),
         sigma.theta=runif(1, 0, 100))
}
## or alternatively something like:
# inits <- list(
#   list(theta=rnorm(J, 0, 90), mu.theta=rnorm(1, 0, 90),
#        sigma.theta=runif(1, 0, 90)),
#   list(theta=rnorm(J, 0, 100), mu.theta=rnorm(1, 0, 100),
#        sigma.theta=runif(1, 0, 100))
#   list(theta=rnorm(J, 0, 110), mu.theta=rnorm(1, 0, 110),
#        sigma.theta=runif(1, 0, 110)))

parameters <- c("theta", "mu.theta", "sigma.theta")

## You may need to edit "bugs.directory",
## also you need write access in the working directory:
schools.sim <- bugs(data, inits, parameters, model.file,
    n.chains=3, n.iter=5000,
    bugs.directory="c:/Program Files/WinBUGS14/")
print(schools.sim)
plot(schools.sim)