runjags (version 2.2.2-4)

read.jagsfile: Extract Any Models, Data, Monitored Variables or Initial Values As Character Vectors from a JAGS or WinBUGS Format Textfile

Description

Read a user specified BUGS or JAGS textfile or character variable and extract any models, data, monitored variables or initial values as character vectors. Used by (auto)run.jags to interpret the input file(s) or strings. This function is more likely to be used via run.jags where the model specified to run.jags is the path used by this function. The read.winbugs function is an alias to read.jagsfile.

Usage

read.jagsfile(file)
read.JAGSfile(file)
read.winbugs(file)
read.WinBUGS(file)

Value

A named list of elements required to compile a model. These can be used to create a call to run.jags, but it would be more usual to call this function directly on the model file.

Arguments

file

either a relative or absolute path to a textfile (including the file extension) containing a model in the JAGS language and possibly monitored variable names, data and/or initial values, or a character string of the same. May also be a vector of paths to different text files, possibly separately containing the model, data and intitial values. No default. The model must be started with the string 'model{' and ended with '}' on new lines. Data must be similarly started with 'data{', monitored variables with 'monitor{', and initial values as 'inits{', and all ended with '}'. Seperate variables in such blocks must be separated by a line break. If multiple models are found, all but the first one are ignored with a warning. Multiple data blocks and monitor blocks are combined, multiple inits blocks are used for different chains. Monitors may also be given using the phrase '#monitor# variable' within the model block, in which case 'variable' is added to the list of monitored variables found in the monitor block(s). The use of automatically generated data and initial values is also supported using similar syntax, with '#data# variable' for automatically generated data variables or '#inits# variable' for automatically generated initial value variables in which case 'variable' is used as data or initial values with a value taken by run.jags from datalist, initlist or R objects as appropriate. '#inits#', '#data#' and '#monitor#' statements can appear on the same line as model code, but no more than one of these statements should be used on the same line. Examples of acceptable model syntax are given below.

Details

There are a number of special strings permitted inside the model specification as follows:

#data# variables to be retrieved from a list or environment

#inits# variables to be retrieved from a list or environment

#monitors# monitored variables to use

#modules# JAGS extension modules optionally also specifying the status (e.g. #modules# glm on, dic on)

#factories# JAGS factories and types required, optionally also specifying the status (e.g. #factories# mix::TemperedMix sampler on)

#response# - a single variable name specifying the response variable (optional)

#residual# - a single variable name specifying a variable that represents the residuals (optional)

#fitted# - a single variable name specifying a variable that represents the fitted value (optional)

#Rdata# when placed inside a data{ } or inits{ } block, this signifies that any arrays indside are in column major order. This is the default for any blocks that are not specified as a list( ).

#BUGSdata# when placed inside a data{ } or inits{ } block, this signifies that any arrays indside are in row major order. This is the default for any blocks that are specified as a list( ), such as those that have been created for use with WinBUGS.

#modeldata# when placed inside a data{ } block, this signifies that the code is to be passed to JAGS along with the model{ } block

References

Lunn D, Jackson C, Best N, Thomas A, Spiegelhalter D (2012). The BUGS book: A practical introduction to Bayesian analysis. CRC press; and Matthew J. Denwood (2016). runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS. Journal of Statistical Software, 71(9), 1-25. doi:10.18637/jss.v071.i09

See Also

run.jags and write.jagsfile for the reverse operation, and possibly an example of the formatting allowed

Examples

# ALL SYNTAX GIVEN BELOW IS EQUIVALENT

# Use a modified WinBUGS text file with manual inits and manual data and
# a seperate monitor block (requires least modification from a WinBUGS
# file).  For compatibility with WinBUGS, the use of list() to enclose
# data and initial values is allowed and ignored, however all seperate
# variables in the data and inits blocks must be seperated with a line
# break (commas or semicolons before linebreaks are ignored).  data{ ... }
# and inits{ ... } must also be added to WinBUGS textfiles so that the
# function can seperate data from initial values.  Iterative loops are
# allowed in data blocks but not in init blocks.  See also the differences
# in JAGS versus WinBUGS syntax in the JAGS help file.

# The examples below are given as character strings for portability,
# but these could also be contained in a separate model file with the
# arguments to read.jagsfile and run.jags specified as the file path


# A model that could be used with WinBUGS, incorporating data and inits.
# A note will be produced that the data and inits are being converted
# from WinBUGS format:

string <- "
model{

	for(i in 1:N){
		Count[i] ~ dpois(mean)
	}
	mean ~ dgamma(0.01, 100)
}

data{
list(
 Count = c(1,2,3,4,5,6,7,8,9,10),
 N = 10
 )
}

inits{
list(
	mean = 1
	)
}

inits{
list(
	mean = 100
	)
}

"

model <- read.winbugs(string)
if (FALSE) {
results <- run.jags(string, monitor='mean')
}


# The same model but specified in JAGS format.  This syntax also defines
# monitors in the model, and uses data retrieved from the R environment:

string <- "
model{

	for(i in 1:N){
		Count[i] ~ dpois(mean) #data# Count, N
	}
	mean ~ dgamma(0.01, 100)
	#monitor# mean
}

inits{
	mean <- 1
}

inits{
	mean <- 100
}
"

model <- read.jagsfile(string)
Count <- 1:10
N <- length(Count)
if (FALSE) {
results <- run.jags(string)
}


# The same model using autoinits and a mixture of manual and autodata:
string <- "
model{

	for(i in 1:N){
		Count[i] ~ dpois(mean) #data# Count
	}
	mean ~ dgamma(0.01, 100)
	#monitor# mean
	#inits# mean
}

data{

	N <- 10

}
"

model <- read.jagsfile(string)
Count <- 1:10
mean <- list(1, 100)
if (FALSE) {
results <- run.jags(string, n.chains=2)
}