| Title: | Bayesian Graphical Models using MCMC |
|---|---|
| Description: | Interface to the JAGS MCMC library. |
| Authors: | Martyn Plummer [aut, cre], Alexey Stukalov [ctb], Matt Denwood [ctb] |
| Maintainer: | Martyn Plummer <[email protected]> |
| License: | GPL (== 2) |
| Version: | 4-16 |
| Built: | 2024-11-01 11:49:48 UTC |
| Source: | CRAN |
The rjags package provides an interface from R to the JAGS library for Bayesian data analysis. JAGS uses Markov Chain Monte Carlo (MCMC) to generate a sequence of dependent samples from the posterior distribution of the parameters.
JAGS is a clone of BUGS (Bayesian analysis Using Gibbs Sampling). See Lunn et al (2009) for a history of the BUGS project. Note that the rjags package does not include a copy of the JAGS library: you must install this separately. For instructions on downloading JAGS, see the home page at https://mcmc-jags.sourceforge.io.
To fully understand how JAGS works, you need to read the JAGS User Manual. The manual explains the basics of modelling with JAGS and shows the functions and distributions available in the dialect of the BUGS language used by JAGS. It also describes the command line interface. The rjags package does not use the command line interface but provides equivalent functionality using R functions.
Analysis using the rjags package proceeds in steps:
Define the model using the BUGS language in a separate file.
Read in the model file using the jags.model function.
This creates an object of class “jags”.
Update the model using the update method for
“jags” objects. This constitutes a ‘burn-in’ period.
Extract samples from the model object using the
coda.samples function. This creates an object of class
“mcmc.list” which can be used to summarize the posterior
distribution. The coda package also provides convergence
diagnostics to check that the output is valid for analysis (see
Plummer et al 2006).
Martyn Plummer
Lunn D, Spiegelhalter D, Thomas A, Best N. (2009) The BUGS project: Evolution, critique and future directions. Statistics in Medicine, 28:3049-67.
Plummer M, Best N, Cowles K, Vines K (2006). CODA: Convergence Diagnosis and Output Analysis for MCMC, R News, 6:7-11.
Update the model in adaptive mode.
adapt(object, n.iter, end.adaptation=FALSE, ...)adapt(object, n.iter, end.adaptation=FALSE, ...)
object |
a |
n.iter |
length of the adaptive phase |
end.adaptation |
logical flag. If |
... |
additional arguments to the update method |
This function is not normally called by the user. It is called by the
jags.model function when the model object is created.
When a JAGS model is compiled, it may require an initial sampling phase during which the samplers adapt their behaviour to maximize their efficiency (e.g. a Metropolis-Hastings random walk algorithm may change its step size). The sequence of samples generated during this adaptive phase is not a Markov chain, and therefore may not be used for posterior inference on the model.
The adapt function updates the model for n.iter
iterations in adaptive mode. Then each sampler reports whether it
has acheived optimal performance (e.g. whether the rejection rate of a
Metropolis-Hasting sampler is close to the theoretical optimum). If
any sampler reports failure of this test then adapt returns
FALSE.
If end.adaptation = TRUE, then adaptive mode is turned off on
exit, and further calls to adapt() do nothing. The model may be
maintained in adaptive mode with the default option end.adaptation =
FALSE so that successive calls to adapt() may be made until
adaptation is satisfactory.
Returns TRUE if all the samplers in the model have successfully
adapted their behaviour to optimum performance and FALSE
otherwise.
Martyn Plummer
This is a wrapper function for jags.samples which sets a trace
monitor for all requested nodes, updates the model, and coerces the
output to a single mcmc.list object.
coda.samples(model, variable.names, n.iter, thin = 1, na.rm=TRUE, ...)coda.samples(model, variable.names, n.iter, thin = 1, na.rm=TRUE, ...)
model |
a jags model object |
variable.names |
a character vector giving the names of variables to be monitored |
n.iter |
number of iterations to monitor |
thin |
thinning interval for monitors |
na.rm |
logical flag that indicates whether variables containing missing values should be omitted. See details. |
... |
optional arguments that are passed to the update method for jags model objects |
If na.rm=TRUE (the default) then elements of a variable that
are missing (NA) for any iteration in at least one chain will
be dropped.
This argument was added to handle incompletely defined variables.
From JAGS version 4.0.0, users may monitor variables that are not
completely defined in the BUGS language description of the model,
e.g. if y[i] is defined in a for loop starting from
i=3 then y[1], y[2] are not defined. The user may still
monitor variable y and the monitored values corresponding to
y[1], y[2] will have value NA for all iterations in all
chains. Most of the functions in the coda package cannot handle
missing values so these variables are dropped by default.
An mcmc.list object.
Martyn Plummer
data(LINE) LINE$recompile() LINE.out <- coda.samples(LINE, c("alpha","beta","sigma"), n.iter=1000) summary(LINE.out)data(LINE) LINE$recompile() LINE.out <- coda.samples(LINE, c("alpha","beta","sigma"), n.iter=1000) summary(LINE.out)
JAGS modules contain factory objects for samplers, monitors, and random number generators for a JAGS model. These functions allow fine-grained control over which factories are active.
list.factories(type) set.factory(name, type, state)list.factories(type) set.factory(name, type, state)
name |
name of the factory to set |
type |
type of factory to query or set. Possible values are
|
state |
a logical. If |
list.factories returns a data frame with two columns, the first
column shows the names of the factory objects in the currently loaded
modules, and the second column is a logical vector indicating whether
the corresponding factory is active or not.
set.factory is called to change the future behaviour of factory
objects. If a factory is set to inactive then it will be skipped.
When a module is loaded, all of its factory objects are active. This is also true if a module is unloaded and then reloaded.
Martyn Plummer
list.factories("sampler") list.factories("monitor") list.factories("rng") set.factory("base::Slice", "sampler", FALSE) list.factories("sampler") set.factory("base::Slice", "sampler", TRUE)list.factories("sampler") list.factories("monitor") list.factories("rng") set.factory("base::Slice", "sampler", FALSE) list.factories("sampler") set.factory("base::Slice", "sampler", TRUE)
Function to extract random samples of the penalized deviance from
a jags model.
dic.samples(model, n.iter, thin = 1, type, ...)dic.samples(model, n.iter, thin = 1, type, ...)
model |
a jags model object |
n.iter |
number of iterations to monitor |
thin |
thinning interval for monitors |
type |
type of penalty to use |
... |
optional arguments passed to the update method for jags model objects |
The dic.samples function generates penalized deviance
statistics for use in model comparison. The two alternative penalized
deviance statistics generated by dic.samples are the deviance
information criterion (DIC) and the penalized expected deviance.
These are chosen by giving the values “pD” and “popt” respectively
as the type argument.
DIC (Spiegelhalter et al 2002) is calculated by adding the “effective
number of parameters” (pD) to the expected deviance. The
definition of pD used by dic.samples is the one proposed
by Plummer (2002) and requires two or more parallel chains in the
model.
DIC is an approximation to the penalized plug-in deviance, which is used when only a point estimate of the parameters is of interest. The DIC approximation only holds asymptotically when the effective number of parameters is much smaller than the sample size, and the model parameters have a normal posterior distribution.
The penalized expected deviance (Plummer 2008) is calculated by adding
the optimism (popt) to the expected deviance. The popt
penalty is at least twice the size of the pD penalty, and
penalizes complex models more severely.
An object of class “dic”. This is a list containing the following elements:
deviance |
A numeric vector, with one element for each observed stochastic node, containing the mean deviance for that node |
penalty |
A numeric vector, with one element for each observed stochastic node, containing an estimate of the contribution towards the penalty |
type |
A string identifying the type of penalty: “pD” or “popt” |
The popt penalty is estimated by importance weighting, and may
be numerically unstable.
Martyn Plummer
Spiegelhalter, D., N. Best, B. Carlin, and A. van der Linde (2002), Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society Series B 64, 583-639.
Plummer, M. (2002), Discussion of the paper by Spiegelhalter et al. Journal of the Royal Statistical Society Series B 64, 620.
Plummer, M. (2008) Penalized loss functions for Bayesian model comparison. Biostatistics doi: 10.1093/biostatistics/kxm049
Compare two models by the difference of two dic objects.
dic1 - dic2 diffdic(dic1, dic2)dic1 - dic2 diffdic(dic1, dic2)
dic1, dic2
|
Objects inheriting from class “dic” |
A diffdic object represents the difference in penalized
deviance between two models. A negative value indicates that
dic1 is preferred and vice versa.
An object of class “diffdic”. This is a numeric vector with an element for each observed stochastic node in the model.
The diffdic class has its own print method, which will
display the sum of the differences, and its sample standard
deviation.
The problem of determining what is a noteworthy difference in DIC (or other penalized deviance) between two models is currently unsolved. Following the results of Ripley (1996) on the Akaike Information Criterion, Plummer (2008) argues that there is no absolute scale for comparison of two penalized deviance statistics, and proposes that the difference should be calibrated with respect to the sample standard deviation of the individual contributions from each observed stochastic node.
Martyn Plummer
Ripley, B. (1996) Statistical Pattern Recognition and Neural Networks. Cambridge University Press.
Plummer, M. (2008) Penalized loss functions for Bayesian model comparison. Biostatistics doi: 10.1093/biostatistics/kxm049
jags.model is used to create an object representing a
Bayesian graphical model, specified with a BUGS-language description
of the prior distribution, and a set of data.
jags.model(file, data, inits, n.chains = 1, n.adapt=1000, quiet=FALSE)jags.model(file, data, inits, n.chains = 1, n.adapt=1000, quiet=FALSE)
file |
the name of the file containing a description of the model in the JAGS dialect of the BUGS language. Alternatively, |
data |
a list or environment containing the data. Any numeric
objects in |
inits |
optional specification of initial values in the form of a
list or a function (see |
n.chains |
the number of parallel chains for the model |
n.adapt |
the number of iterations for adaptation. See
|
quiet |
if |
jags.model returns an object inheriting from class jags
which can be used to generate dependent samples from the posterior
distribution of the parameters
An object of class jags is a list of functions that share a
common environment. This environment encapsulates the state of the
model, and the functions can be used to query or modify the model
state.
ptr() |
Returns an external pointer to an object created by the JAGS library |
data() |
Returns a list containing the data that define the observed nodes in the model |
model() |
Returns a character vector containing the BUGS-language representation of the model |
state(internal=FALSE) |
Returns a list of length equal to the
number of parallel chains in the model. Each element of the list is
itself a list containing the current parameter values in that chain.
if |
update(n.iter) |
Updates the model by |
There are various ways to specify initial values for a JAGS model. If no initial values are supplied, then they will be generated automatically by JAGS. See the JAGS User Manual for details. Otherwise, the options are as follows:
A list of numeric values. Initial values for a single chain may supplied as a named list of numeric values. If there are multiple parallel chains then the same list is re-used for each chain.
A list of lists. Distinct initial values for each chain may be given as a list of lists. In this case, the list should have the same length as the number of chains in the model.
A function. A function may be supplied that returns a list of
initial values. The function is called repeatedly to generate initial
values for each chain. Normally this function should call some random
number generating functions so that it returns different values every
time it is called. The function should either have no arguments, or
have a single argument named chain. In the latter case, the
supplied function is called with the chain number as argument. In this
way, initial values may be generated that depend systematically on
the chain number.
Each chain in a model has its own random number generator (RNG). RNGs and their initial seed values are assigned automatically when the model is created. The automatic seeds are calculated from the current time.
If you wish to make the output from the model reproducible, you may
specify the RNGs to be used for each chain, and their starting seeds
as part of the inits argument (see Initialization
above). This is done by supplementing the list of initial parameter
values for a given chain with two additional elements named
“.RNG.name”, and “.RNG.seed”:
.RNG.namea character vector of length 1. The names of the RNGs supplied in the base module are:
“base::Wichmann-Hill”
“base::Marsaglia-Multicarry”
“base::Super-Duper”
“base::Mersenne-Twister”
If the lecuyer module is loaded, it provides “lecuyer::RngStream”
.RNG.seeda numeric vector of length 1 containing an integer value.
Note that it is also possible to specify “.RNG.state” rather than
“.RNG.seed” - see for example the output of parallel.seeds
Martyn Plummer
A JAGS module is a dynamically loaded library that extends the functionality of JAGS. These functions load and unload JAGS modules and show the names of the currently loaded modules.
load.module(name, path, quiet=FALSE) unload.module(name, quiet=FALSE) list.modules()load.module(name, path, quiet=FALSE) unload.module(name, quiet=FALSE) list.modules()
name |
name of the load module to be loaded |
path |
file path to the location of the DLL. If omitted,
the option |
quiet |
a logical. If |
Martyn Plummer
list.modules() load.module("glm") list.modules() unload.module("glm") list.modules()list.modules() load.module("glm") list.modules() unload.module("glm") list.modules()
A jags object represents a Bayesian graphical model
described using the BUGS language.
## S3 method for class 'jags' coef(object, chain=1, ...) ## S3 method for class 'jags' variable.names(object, ...) list.samplers(object)## S3 method for class 'jags' coef(object, chain=1, ...) ## S3 method for class 'jags' variable.names(object, ...) list.samplers(object)
object |
a |
chain |
chain number to query |
... |
additional arguments to the call (ignored) |
The coef function returns a list with an entry for each Node
array that contains an unobserved Node. Elements corresponding to
observed Nodes or deterministic Nodes are given missing values.
The variable.names function returns a character vector of
names of node arrays used in the model.
The list.samplers function returns a named list with an entry
for each Sampler used by the model. Each list element is a character
vector containing the names of stochastic Nodes that are updated
together in a block. The names of the list elements indicate the
sampling methods that are used to update each block. Stochastic nodes
that are updated by forward sampling from the prior are not listed.
Martyn Plummer
data(LINE) LINE$recompile() coef(LINE) variable.names(LINE) list.samplers(LINE)data(LINE) LINE$recompile() coef(LINE) variable.names(LINE) list.samplers(LINE)
Function to extract random samples from the posterior distribution
of the parameters of a jags model.
jags.samples(model, variable.names, n.iter, thin = 1, type="trace", force.list=FALSE, ...)jags.samples(model, variable.names, n.iter, thin = 1, type="trace", force.list=FALSE, ...)
model |
a jags model object |
variable.names |
a character vector giving the names of variables to be monitored |
n.iter |
number of iterations to monitor |
thin |
thinning interval for monitors |
type |
type of monitor (can be vectorised) |
force.list |
option to consistently return a named list of monitor types even if a single monitor type is requested |
... |
optional arguments passed to the update method for jags model objects |
The jags.samples function creates monitors for the given
variables, runs the model for n.iter iterations and returns
the monitored samples.
A list of mcarray objects, with one element for each
element of the variable.names argument. If more than
one type of monitor is requested (or if force.list is TRUE)
then the return value will be a (named) list of lists of
mcarray objects, with one element for each monitor type.
Martyn Plummer
data(LINE) LINE$recompile() LINE.samples <- jags.samples(LINE, c("alpha","beta","sigma"), n.iter=1000) LINE.samples LINE.samples <- jags.samples(LINE, c("alpha","beta","sigma"), force.list=TRUE, n.iter=1000) LINE.samples LINE.samples <- jags.samples(LINE, c("alpha","alpha"), n.iter=1000, type=c("trace","mean")) LINE.samples$trace LINE.samples$meandata(LINE) LINE$recompile() LINE.samples <- jags.samples(LINE, c("alpha","beta","sigma"), n.iter=1000) LINE.samples LINE.samples <- jags.samples(LINE, c("alpha","beta","sigma"), force.list=TRUE, n.iter=1000) LINE.samples LINE.samples <- jags.samples(LINE, c("alpha","alpha"), n.iter=1000, type=c("trace","mean")) LINE.samples$trace LINE.samples$mean
Get the version of the JAGS library which is currently linked to this R session
jags.version()jags.version()
The version of JAGS formatted as a package version string (see package_version)
The LINE model is a trivial linear regression model with only 5 observations. It's main use is to allow automated checks of the rjags package.
A jags.model object, which must be recompiled before
use.
An mcarray object is used by the jags.samples function
to represent MCMC output from a JAGS model. It is an array with named
dimensions, for which the dimensions "iteration" and "chain" have a
special status
## S3 method for class 'mcarray' summary(object, FUN, ...) ## S3 method for class 'mcarray' print(x, ...) ## S3 method for class 'mcarray' as.mcmc.list(x, ...)## S3 method for class 'mcarray' summary(object, FUN, ...) ## S3 method for class 'mcarray' print(x, ...) ## S3 method for class 'mcarray' as.mcmc.list(x, ...)
object, x
|
an |
FUN |
a function to be used to generate summary statistics |
... |
additional arguments to the call |
The coda package defines mcmc objects for representing
output from an MCMC sampler, and mcmc.list for representing
output from multiple parallel chains. These objects emphasize the
time-series aspect of the MCMC output, but lose the original array
structure of the variables they represent. The mcarray class
attempts to rectify this by preserving the dimensions of the original
node array defined in the JAGS model.
The summary method for mcarray objects applies the
given function to the array, marginalizing the "chain" and "iteration"
dimensions.
The print method applies the summary function with
FUN=mean.
The as.mcmc.list method coerces an mcarray to an
mcmc.list object so that the diagnostics provided by the
coda package can be applied to the MCMC output it represents.
Martyn Plummer
On a multi-processor system, you may wish to run parallel chains using
multiple jags.model objects, each running a single chain on a
separate processor. This function returns a list of values that may
be used to initialize the random number generator of each chain.
parallel.seeds(factory, nchain)parallel.seeds(factory, nchain)
factory |
Name of the RNG factory to use. |
nchain |
Number of chains for which to initialize RNGs. |
parallel.seeds returns a list of RNG states. Each element
is a list of length 2 with the following elements:
.RNG.name |
The name of the RNG |
.RNG.state |
An integer vector giving the state of the RNG. |
It is not yet possible to make the results of parallel.seeds
reproducible. This will be fixed in a future version of JAGS.
Martyn Plummer
jags.model, section “Random number generators”,
for further details on RNG initialization;
list.factories to find the names of available RNG
factories.
##The BaseRNG factory generates up to four distinct types of RNG. If ##more than 4 chains are requested, it will recycle the RNG types, but ##use different initial values parallel.seeds("base::BaseRNG", 3) ## The lecuyer module provides the RngStream factory, which allows large ## numbers of independent parallel RNGs to be generated. load.module("lecuyer") list.factories(type="rng") parallel.seeds("lecuyer::RngStream", 5);##The BaseRNG factory generates up to four distinct types of RNG. If ##more than 4 chains are requested, it will recycle the RNG types, but ##use different initial values parallel.seeds("base::BaseRNG", 3) ## The lecuyer module provides the RngStream factory, which allows large ## numbers of independent parallel RNGs to be generated. load.module("lecuyer") list.factories(type="rng") parallel.seeds("lecuyer::RngStream", 5);
Read data for a JAGS model from a file.
read.jagsdata(file) read.bugsdata(file)read.jagsdata(file) read.bugsdata(file)
file |
name of a file containing a text repesentation of the data for a jags model |
The command line interface for JAGS reads data and initial values from
a text file. The data format used for jags data files is the same as the R
dump function. Thus the data values can be read into an
R session using the source function, but this will create
objects in the global environment. The read.jagsdata function
is a simple wrapper that reads the data into a list instead.
OpenBUGS also reads data and initial values from a text file. The
format of these files is described as "S-PLUS" format by the OpenBUGS
authors. It superficially resembles the format used by the dput
function (and in fact can be parsed by the dget
function). However, in BUGS "S-PLUS" format, arrays are stored in
row-major order instead of the column-major order used by R. The
read.bugsdata function reads OpenBUGS "S-PLUS" format files and
permutes the elements of arrays so that they appear in the correct
order.
Either function returns a list which can be used as the
data or inits argument of jags.model.
A named list of numeric vectors or arrays.
Earlier versions of the rjags package had a read.data function
which read data in either format, but the function name was ambiguous
(There are many data file format in R) so this is now deprecated.
Martyn Plummer
These functions are provided for compatibility with older versions of the rjags package and will soon be defunct.
read.data(file, format=c("jags","bugs"))read.data(file, format=c("jags","bugs"))
file |
name of a file containing a text repesentation of the data for a jags model |
format |
format of the data |
read.data with format="jags" is a deprecated synonym for
read.jagsdata and with
format="bugs" is a deprecated synonym for
read.bugsdata.
Update the Markov chain associated with the model.
## S3 method for class 'jags' update(object, n.iter=1, by, progress.bar, ...)## S3 method for class 'jags' update(object, n.iter=1, by, progress.bar, ...)
object |
a |
n.iter |
number of iterations of the Markov chain to run |
by |
refresh frequency for progress bar. See Details |
progress.bar |
type of progress bar. Possible values are
|
... |
additional arguments to the update method (ignored) |
Since MCMC calculations are typically long, a progress bar is
displayed during the call to update. The type of progress bar
is determined by the progress.bar argument. Type "text"
is displayed on the R console. Type "gui" is a graphical
progress bar in a new window. The progress bar is suppressed if
progress.bar is "none" or NULL, if the update is
less than 100 iterations, or if R is not running interactively.
The default progress bar type is taken from the option jags.pb.
The progress bar is refreshed every by iterations. The
update can only be interrupted when the progress bar is refreshed.
Therefore it is advisable not to set by to a very large
value. By default by is either n.iter/50 or 100,
whichever is smaller.
The update method for jags model objects modifies the
original object and returns NULL.
Martyn Plummer