WinBUGS User Manual - MRC Biostatistics Unit

WinBUGS User Manual - MRC Biostatistics Unit
WinBUGS User Manual
Version 1.4, January 2003
David Spiegelhalter1
Andrew Thomas2
Nicky Best2
Dave Lunn2
1 MRC Biostatistics Unit,
Institute of Public Health,
Robinson Way,
Cambridge CB2 2SR, UK
2 Department of Epidemiology & Public Health,
Imperial College School of Medicine,
Norfolk Place,
London W2 1PG, UK
e-mail:
[email protected]
[email protected]
[general]
[technical]
internet: http://www.mrc-bsu.cam.ac.uk/bugs
Permission and Disclaimer
please click here to read the legal bit
More informally, potential users are reminded to be extremely careful if using this program for serious statistical
analysis. We have tested the program on quite a wide set of examples, but be particularly careful with types of
model that are currently not featured. If there is a problem, WinBUGS might just crash, which is not very good,
but it might well carry on and produce answers that are wrong, which is even worse. Please let us know of any
successes or failures.
Beware: MCMC sampling can be dangerous!
Contents
Introduction Ł
This manual
Advice for new users
MCMC methods
How WinBUGS syntax differs from that of ClassicBUGS
Changes from WinBUGS 1.3
Compound Documents Ł
What is a compound document?
Working with compound documents
Editing compound documents
Compound documents and e-mail
Printing compound documents and Doodles
Reading in text files
Model Specification Ł
Graphical models
Graphs as a formal language
The BUGS language: stochastic nodes
Censoring and truncation
Constraints on using certain distributions
Logical nodes
Arrays and indexing
Repeated structures
Data transformations
Nested indexing and mixtures
Formatting of data
DoodleBUGS: The Doodle Editor Ł
General properties
Creating a node
Selecting a node
Deleting a node
Moving a node
Creating a plate
Selecting a plate
Deleting a plate
Moving a plate
Resizing a plate
Creating an edge
Deleting an edge
Moving a Doodle
Resizing a Doodle
Printing a Doodle
The Model Menu Ł
General properties
Specification...
Update...
Monitor Metropolis
Save State
Seed...
Script
The Inference Menu Ł
General properties
Samples...
Compare...
Correlations...
Summary...
Rank...
DIC...
The Info Menu Ł
General properties
Open Log
Clear Log
Node info...
Components
The Options Menu Ł
Output options...
Blocking options...
Update options...
Batch-mode: Scripts
Tricks: Advanced Use of the BUGS Language Ł
Specifying a new sampling distribution
Specifying a new prior distribution
Specifying a discrete prior on a set of values
Using pD and DIC
Mixtures of models of different complexity
Where the size of a set is a random quantity
Assessing sensitivity to prior assumptions
Modelling unknown denominators
Handling unbalanced datasets
Learning about the parameters of a Dirichlet distribution
Use of the "cut" function
WinBUGS Graphics Ł
General properties
Margins
Axis Bounds
Titles
All Plots
Fonts
Specific properties (via Special...)
Density plot
Box plot
Caterpillar plot
Model fit plot
Scatterplot
Tips and Troubleshooting Ł
Restrictions when modelling
Some error messages
Some Trap messages
The program hangs
Speeding up sampling
Improving convergence
Tutorial Ł
Introduction
Specifying a model in the BUGS language
Running a model in WinBUGS
Monitoring parameter values
Checking convergence
How many iterations after convergence?
Obtaining summaries of the posterior distribution
Changing MCMC Defaults (advanced users only) Ł
Defaults for numbers of iterations
Defaults for sampling methods
Distributions Ł
Discrete Univariate
Continuous Univariate
Discrete Multivariate
Continuous Multivariate
References
Introduction
Contents
This manual
Advice for new users
MCMC methods
How WinBUGS syntax differs from that of Classic BUGS
Changes from WinBUGS 1.3
This manual
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This manual describes the WinBUGS software − an interactive Windows version of the BUGS program for
Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) techniques.
WinBUGS allows models to be described using a slightly amended version of the BUGS language, or as
Doodles (graphical representations of models) which can, if desired, be translated to a text-based description.
The BUGS language is more flexible than the Doodles.
The sections cover the following topics:
Introduction: the software and how a new user can start using WinBUGS. Differences with previous
incarnations of BUGS and WinBUGS are described.
Compound Documents: the use of the compound document interface that underlies the program, showing
how documents can be created, edited and manipulated.
Model Specification: the role of graphical models and the specification of the BUGS language.
DoodleBUGS: The Doodle Editor: the DoodleBUGS software which allows complex Bayesian models to be
specified as Doodles using a graphical interface.
The Model Menu: the Model Menu permits models expressed as either Doodles or in the BUGS language to
be parsed, checked and compiled.
The Inference Menu: the Inference Menu controls the monitoring, display and summary of the simulated
variables: tools include specialized graphics and space-saving short-cuts for simple summaries of large
numbers of variables.
The Info Menu: the Info menu provides a log of the run and other information.
The Options Menu: facility that allows the user some control over where the output is displayed and the
various available MCMC algorithms.
Batch-mode: Scripts: how to run WinBUGS in batch-mode using 'scripts'.
Tricks: Advanced Use of the BUGS Language: special tricks for dealing with non-standard problems, e.g.
specification of arbitrary likelihood functions.
WinBUGS Graphics: how to display and change the format of graphical output.
Tips and Troubleshooting: tips and troubleshooting advice for frequently experienced problems.
Tutorial: a tutorial for new users.
Changing MCMC Defaults (advanced users only): how to change some of the default settings for the MCMC
algorithms used in WinBUGS.
Distributions: lists the various (closed-form) distributions available in WinBUGS.
References: references to relevant publications.
Users are advised that this manual only concerns the syntax and functionality of WinBUGS, and does
not deal with issues of Bayesian reasoning, prior distributions, statistical modelling, monitoring
convergence, and so on. If you are new to MCMC, you are strongly advised to use this software in
conjunction with a course in which the strengths and weaknesses of this procedure are described. Please note
the disclaimer at the beginning of this manual.
There is a large literature on Bayesian analysis and MCMC methods. For further reading, see, for example,
Carlin and Louis (1996), Gelman et al (1995), Gilks, Richardson and Spiegelhalter (1996): Brooks (1998)
provides an excellent introduction to MCMC. Chapter 9 of the Classic BUGS manual, 'Topics in Modelling',
discusses 'non-informative' priors, model criticism, ranking, measurement error, conditional likelihoods,
parameterisation, spatial models and so on, while the CODA documentation considers convergence
diagnostics. Congdon (2001) shows how to analyse a very wide range of models using WinBUGS. The BUGS
website provides additional links to sites of interest, some of which provide extensive examples and tutorial
material.
Note that WinBUGS simulates each node in turn: this can make convergence very slow and the program very
inefficient for models with strongly related parameters, such as hidden-Markov and other time series structures.
If you have the educational version of WinBUGS, you can run any model on the example data-sets provided
(except possibly some of the newer examples). If you want to analyse your own data you will only be able to
build models with less than 100 nodes (including logical nodes). However, the key for removing this restriction
can be obtained by registering via the BUGS website, from which the current distribution policy can also be
obtained.
Advice for new users
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Although WinBUGS can be used without further reference to any of the BUGS project, experience with using
Classic BUGS may be an advantage, and certainly the documentation on BUGS Version 0.5 and 0.6 (available
from http://www.mrc-bsu.cam.ac.uk/bugs) contains examples and discussion on wider issues in modelling
using MCMC methods. If you are using WinBUGS for the first time, the following stages might be reasonable:
1. Step through the simple worked example in the tutorial.
2. Try other examples provided with this release (see Examples Volume 1 and Examples Volume 2)
3. Edit the BUGS language to fit an example of your own.
If you are interested in using Doodles:
4. Try editing an existing Doodle (e.g. from Examples Volume 1), perhaps to fit a problem of your own.
5. Try constructing a Doodle from scratch.
Note that there are many features in the BUGS language that cannot be expressed with Doodles. If you wish to
proceed to serious, non-educational use, you may want to dispense with DoodleBUGS entirely, or just use it
for initially setting up a simplified model that can be elaborated later using the BUGS language. Unfortunately
we do not have a program to back-translate from a text-based model description to a Doodle!
MCMC methods
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Users should already be aware of the background to Bayesian Markov chain Monte Carlo methods: see for
example Gilks et al (1996). Having specified the model as a full joint distribution on all quantities, whether
parameters or observables, we wish to sample values of the unknown parameters from their conditional
(posterior) distribution given those stochastic nodes that have been observed. The basic idea behind the Gibbs
sampling algorithm is to successively sample from the conditional distribution of each node given all the others
in the graph (these are known as full conditional distributions): the Metropolis-within-Gibbs algorithm is
appropriate for difficult full conditional distributions and does not necessarily generate a new value at each
iteration. It can be shown that under broad conditions this process eventually provides samples from the joint
posterior distribution of the unknown quantities. Empirical summary statistics can be formed from these
samples and used to draw inferences about their true values.
The sampling methods are used in the following hierarchies (in each case a method is only used if no previous
method in the hierarchy is appropriate):
Continuous target distribution
Method
Conjugate
Log-concave
Restricted range
Unrestricted range
Direct sampling using standard algorithms
Derivative-free adaptive rejection sampling (Gilks, 1992)
Slice sampling (Neal, 1997)
Current point Metropolis
Discrete target distribution
Method
Finite upper bound
Shifted Poisson
Inversion
Direct sampling using standard algorithm
In cases where the graph contains a Generalized Linear Model (GLM) component, it is possible to request (see
Blocking options...) that WinBUGS groups (or 'blocks') together the fixed-effect parameters and updates them
via the multivariate sampling technique described in Gamerman (1997). This is essentially a MetropolisHastings algorithm where at each iteration the proposal distribution is formed by performing one iteration,
starting at the current point, of Iterative Weighted Least Squares (IWLS).
If WinBUGS is unable to classify the full conditional for a particular parameter (p, say) according to the above
hierarchy, then an error message will be returned saying "Unable to choose update method for p".
Simulations are carried out univariately, except for explicitly defined multivariate nodes and, if requested, blocks
of fixed-effect parameters in GLMs (see above). There is also the option of using ordered over-relaxation (Neal,
1998), which generates multiple samples at each iteration and then selects one that is negatively correlated
with the current value. The time per iteration will be increased, but the within-chain correlations should be
reduced and hence fewer iterations may be necessary. However, this method is not always effective and should
be used with caution.
A slice-sampling algorithm is used for non log-concave densities on a restricted range. This has an adaptive
phase of 500 iterations which will be discarded from all summary statistics.
The current Metropolis MCMC algorithm is based on a symmetric normal proposal distribution, whose standard
deviation is tuned over the first 4000 iterations in order to get an acceptance rate of between 20% and 40%. All
summary statistics for the model will ignore information from this adapting phase.
It is possible for the user to change some aspects of the various available MCMC updating algorithms, such as
the length of an adaptive phase − please see Update options... for details. It is also now possible to change the
sampling methods for certain classes of distribution, although this is delicate and should be done carefully −
see Changing MCMC Defaults (advanced users only) for details.
The shifted Poisson distribution occurs when a Poisson prior is placed on the order of a single binomial
observation.
How WinBUGS syntax differs from that of Classic BUGS
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Changes to the BUGS syntax have been kept, as far as possible, to simplifications. There is now:
- No need for constants (these are declared as part of the data).
- No need for variable declaration (but all names used to declare data must appear in the model).
- No need to specify files for data and initial values.
- No limitation on dimensionality of arrays.
- No limitation on size of problems (except those dictated by hardware).
- No need for semi-colons at end of statements (these were never necessary anyway!)
A major change from the Classic BUGS syntax is that when defining multivariate nodes, the range of the
variable must be explicitly defined: for example
x[1:K] ~ dmnorm(mu[], tau[,])
must be used instead of x[]
say
~ dmnorm(mu[], tau[,]), and for precision matrices you must write,
tau[1:K, 1:K] ~ dwish(R[,], 3)
rather than tau[,]
~ dwish(R[,], 3).
The following format must now be used to invert a matrix:
sigma[1:K, 1:K] <- inverse(tau[,])
Note that inverse(.) is now a vector-valued function as opposed to the relatively inefficient componentwise evaluation required in previous versions of the software.
To convert Classic BUGS files to run under WinBUGS:
a) Open the .bug file as a text file, delete unnecessary declarations, and save as an .odc document.
b) Open .dat files: data has to be formatted as described in Formatting of data: eg
* matrices in data files need to have the full '.structure' format
* all data in datafile need to be described in the model
* need data list of constants and file sizes
* need column headings on rectangular arrays
The data can be copied into the .odc file, or kept as a separate file.
c) Copy the contents of the .in file into the .odc file.
Changes from WinBUGS 1.3
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- modular on-line manual;
- ability to run in batch-mode using scripts;
- running of default script on start-up to allow calling from other programs;
- new graphics (see here, for example) and editing of graphics − note that graphics from previous versions of the
software will be incompatible with this version (1.4);
- missing data and range constraints allowed for multivariate normal;
- new distributions: negative binomial, generalized gamma, multivariate Student-t;
- DIC menu option for model comparison;
- Options menu, for advanced control of MCMC algorithms, for example;
- new syntax for (more efficient) 'inverse' function;
- "interp.lin" interpolation function, "cut" function;
- recursively- (and thus efficiently-) calculated running quantiles;
- MCMC algorithms: block updating of fixed effects − see here and/or here for details;
- non-integer binomial and Poisson data;
- Poisson as prior for continuous quantity;
- 'coverage' of random number generator;
- additional restrictions: END command for rectangular arrays;
- spatial (CAR) models moved to GeoBUGS;
- new display options;
- now possible to print out posterior correlation coefficients for monitored variables;
- new manual sections: Batch-mode: Scripts, Tricks, WinBUGS Graphics, Tutorial, and
Changing MCMC Defaults.
Compound Documents
Contents
What is a compound document?
Working with compound documents
Editing compound documents
Compound documents and e-mail
Printing compound documents and Doodles
Reading in text files
What is a compound document?
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A compound document contains various types of information (formatted text, tables, formulae, plots, graphs
etc) displayed in a single window and stored in a single file. The tools needed to create and manipulate these
information types are always available, so there is no need to continuously move between different programs.
The WinBUGS software has been designed so that it produces output directly to a compound document and
can get its input directly from a compound document. To see an example of a compound document click here.
WinBUGS is written in Component Pascal using the BlackBox development framework: see
http://www.oberon.ch.
In WinBUGS a document is a description of a statistical analysis, the user interface to the software, and the
resulting output.
Compound documents are stored with the .odc extension.
Working with compound documents
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A compound document is like a word-processor document that contains special rectangular embedded regions
or elements, each of which can be manipulated by standard word-processing tools -- each rectangle behaves
like a single large character, and can be focused, selected, moved, copied, deleted etc. If an element is
focused the tools to manipulate its interior become available.
The WinBUGS software works with many different types of elements, the most interesting of which are
Doodles, which allow statistical models to be described in terms of graphs. DoodleBUGS is a specialised
graphics editor and is described fully in DoodleBUGS: The Doodle Editor. Other elements are rather simpler
and are used to display plots of an analysis.
Editing compound documents
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WinBUGS contains a built-in word processor, which can be used to manipulate any output produced by the
software. If a more powerful editing tool is needed WinBUGS documents or parts of them can be pasted into a
standard OLE enabled word processor.
Text is selected by holding down the left mouse button while dragging the mouse over a region of text.
Warning: if text is selected and a key pressed the selection will be replaced by the character typed. The
selection can be removed by pressing the "Esc" key or clicking the mouse.
A single element can be selected by clicking once into it with the left mouse button. A selected element is
distinguished by a thin bounding rectangle. If this bounding rectangle contains small solid squares at the
corners and mid sides it can be resized by dragging these with the mouse. An element can be focused by
clicking twice into it with the left mouse button. A focused element is distinguished by a hairy grey bounding
rectangle.
A selection can be moved to a new position by dragging it with the mouse. To copy the selection hold down the
"control" key while releasing the mouse button.
These operations work across windows and across applications, and so the problem specification and the
output can both be pasted into a single document, which can then be copied into another word-processor or
presentation package.
The style, size, font and colour of selected text can be changed using the Attributes menu. The vertical offset
of the selection can be changed using the Text menu.
The formatting of text can be altered by embedding special elements. The most common format control is the
ruler: pick option Show Marks in menu Text to see what rulers look like. The small black up-pointing triangles
are tab stops, which can be moved by dragging them with the mouse and removed by dragging them outside
the left or right borders of the ruler. The icons above the scale control, for example, centering and page breaks.
Vertical lines within tables can be curtailed by inserting a ruler and removing the lines by selecting each tabstop and then ctrl-left-mouse-click. (Warning: removing the left-most line requires care: there is a tab-stop
hidden behind the upper left-most one that can cause a crash if deleted in the usual way - it seems to require a
ctrl-right-mouse-click!).
Compound documents and e-mail
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WinBUGS compound documents contain non-ascii characters, but the Tools menu contains a command
Encode Document which produces an ascii representation of the focus document. The original document can
be recovered from this encoded form by using the Decode command of the Tools menu. This allows, for
example, Doodles to be sent by e-mail.
Printing compound documents and Doodles
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These can be printed directly from the File menu. If postscript versions of Doodles or whole documents are
wanted, you could install a driver for a postscript printer (say Apple LaserWriter), but set it up to print to file
(checking the paper size is appropriate). Alternatively Doodles or documents could be copied to a presentation
or word-processing package and printed from there.
Reading in text files
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Open these from the File menu as text files. They can be copied into documents, or stored as documents.
Model Specification
Contents
Graphical models
Graphs as a formal language
The BUGS language: stochastic nodes
Censoring and truncation
Constraints on using certain distributions
Logical nodes
Arrays and indexing
Repeated structures
Data transformations
Nested indexing and mixtures
Formatting of data
Graphical models
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We strongly recommend that the first step in any analysis should be the construction of a directed graphical
model. Briefly, this represents all quantities as nodes in a directed graph, in which arrows run into nodes from
their direct influences (parents). The model represents the assumption that, given its parent nodes pa[v], each
node v is independent of all other nodes in the graph except descendants of v, where descendant has the
obvious definition.
Nodes in the graph are of three types.
1. Constants are fixed by the design of the study: they are always founder nodes (i.e. do not have parents),
and are denoted as rectangles in the graph. They must be specified in a data file.
2. Stochastic nodes are variables that are given a distribution, and are denoted as ellipses in the graph;
they may be parents or children (or both). Stochastic nodes may be observed in which case they are
data, or may be unobserved and hence be parameters, which may be unknown quantities underlying a
model, observations on an individual case that are unobserved say due to censoring, or simply missing
data.
3. Deterministic nodes are logical functions of other nodes.
Quantities are specified to be data by giving them values in a data file, in which values for constants are also
given.
Directed links may be of two types: a solid arrow indicates a stochastic dependence while a hollow arrow
indicates a logical function. An undirected dashed link may also be drawn to represent an upper or lower
bound.
Repeated parts of the graph can be represented using a 'plate', as shown below for the range (i in 1:N).
index:
i
from:
alpha
1
up to:
beta
mu[i]
N
tau
sigma
Y[i]
for(i IN 1 : N)
A simple graphical model, where Y[i] depends on mu[i] and tau, with mu[i]
being a logical function of alpha and beta.
The conditional independence assumptions represented by the graph mean that the full joint distribution of all
quantities V has a simple factorisation in terms of the conditional distribution p(v | parents[v]) of each node
given its parents, so that
p(V) =
Π p(v | parents[v])
v in V
The crucial idea is that we need only provide the parent-child distributions in order to fully specify the model,
and WinBUGS then sorts out the necessary sampling methods directly from the expressed graphical structure.
Graphs as a formal language
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A special drawing tool DoodleBUGS has been developed for specifying graphical models, which uses a hyperdiagram approach to add extra information to the graph to give a complete model specification. Each stochastic
and logical node in the graph must be given a name using the conventions explained in Creating a node.
name:
mean
Y[i]
mu[i]
type:
precision
stochastic
tau
alpha
density:
dnorm
lower bound
beta
upper bound
tau
mu[i]
sigma
Y[i]
for(i IN 1 : N)
.
The shaded node Y[i] is normally distributed with mean mu[i] and
precision tau.
name:
mu[i]
value:
alpha - beta * (x[i] - mean(x[]))
type:
alpha
logical
beta
link:
identity
tau
mu[i]
sigma
Y[i]
for(i IN 1 : N)
The shaded node mu[i] is a logical function of alpha, beta, and the
constants x. (x is not required to be shown in the graph).
The value function of a logical node contains all the necessary information to define the logical node: the logical
links in the graph are not strictly necessary.
As an alternative to the Doodle representation, the model can be specified using the text-based BUGS
language, headed by the model statement:
model {
}
text-based description of graph in BUGS language
The BUGS language: stochastic nodes
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In the text-based model description, stochastic nodes are represented by the node name followed by a twiddles
symbol followed by the distribution name followed by a comma-separated list of parents enclosed in brackets
e.g.
r ~ dbin(p, n)
The distributions that can be used in WinBUGS are described in Distributions. Clicking on the name of each
distribution should provide a link to an example of its use provided with this release. The parameters of a
distribution must be explicit nodes in the graph (scalar parameters can also be numerical constants) and so
may not be function expressions.
For distributions not featured in Distributions, see Tricks: Advanced Use of the BUGS Language.
Censoring and truncation
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Censoring is denoted using the notation I(lower, upper) e.g.
x ~ ddist(theta)I(lower, upper)
would denote a quantity x from distribution ddist with parameters theta, which had been observed to lie
between lower and upper. Leaving either lower or upper blank corresponds to no limit, e.g.
I(lower,) corresponds to an observation known to lie above lower. Whenever censoring is specified the
censored node contributes a term to the full conditional distribution of its parents. This structure is only of use if
x has not been observed (if x is observed then the constraints will be ignored).
It is vital to note that this construct does NOT correspond to a truncated distribution, which generates a
likelihood that is a complex function of the basic parameters. Truncated distributions might be handled by
working out an algebraic form for the likelihood and using the techniques for arbitrary distributions described in
Tricks: Advanced Use of the BUGS Language.
It is also important to note that if x, theta, lower and upper are all unobserved, then lower and
upper must not be functions of theta.
Constraints on using certain distributions
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Contiguous elements: Multivariate nodes must form contiguous elements in an array. Since the final element
in an array changes fastest, such nodes must be defined as the final part of any array. For example, to define a
set of K * K Wishart variables as a single multidimensional array x[i,j,k], we could write:
for (i in 1:I) {
x[i, 1:K, 1:K] ~ dwish(R[i,,], 3)
}
where R[i,,] is an array of specified prior parameters.
No missing data: Data defined as multinomial or as multivariate Student-t must be complete, in that missing
values are not allowed in the data array. We realise this is an unfortunate restriction and we hope to relax it in
the future. For multinomial data, it may be possible to get round this problem by re-expressing the multivariate
likelihood as a sequence of conditional univariate binomial distributions.
Note that multivariate normal data may now be specified with missing values.
Conjugate updating: Dirichlet and Wishart distributions can only be used as parents of multinomial and
multivariate normal nodes respectively.
Parameters you can't learn about and must specify as constants: The parameters of Dirichlet and Wishart
distributions and the order (N) of the multinomial distribution must be specified and cannot be given prior
distributions. (There is, however, a trick to avoid this constraint for the Dirichlet distribution − see here.)
Structured precision matrices for multivariate normals: these can be used in certain circumstances. If a
Wishart prior is not used for the precision matrix of a multivariate normal node, then the elements of the
precision matrix are updated univariately without any check of positive-definiteness. This will result in a crash
unless the precision matrix is parameterised appropriately. This is the user's responsibility!
Non-integer data for Poisson and binomial: Previously only integer-valued data were allowed with Poisson
and binomial distributions − this restriction has now been lifted. More generally, it is now possible to specify a
Poisson prior for any continuous quantity.
Range constraints − using the I(.,.) notation − cannot be used with multivariate nodes: except for
multivariate normal distributions in which case the arguments to the I(.,.) function may be specified as
'blanks' or as vector-valued bounds.
Logical nodes
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Logical nodes are represented by the node name followed by a left pointing arrow followed by a logical
expression of its parent nodes e.g.
mu[i] <- beta0 + beta1 * z1[i] + beta2 * z2[i] + b[i]
Logical expressions can be built using the following operators: plus, multiplication, minus, division and unitary
minus. The functions in Table I below can also be used in logical expressions.
In Table I, function arguments represented by e can be expressions, those by s must be scalar-valued nodes in
the graph and those represented by v must be vector-valued nodes in a graph.
Table I: Functions
abs(e)
cloglog(e)
cos(e)
cut(e)
equals(e1, e2)
exp(e)
inprod(v1, v2)
|e|
ln(−ln(1 − e))
cosine(e)
cuts edges in the graph − see Use of the "cut" function
1 if e1 = e2; 0 otherwise
exp(e)
interp.lin(e, v1, v2)
v2p + (v2p + 1 − v2p) * (e − v1p) / (v1p + 1 − v1p)
where the elements of v1 are in ascending order
and p is such that v1p < e < v1p + 1
Σiv1i v2i
inverse(v)
log(e)
logdet(v)
logfact(e)
loggam(e)
logit(e)
max(e1, e2)
mean(v)
v−1
for symmetric positive-definite matrix v
ln(e)
ln(det(v)) for symmetric positive-definite v
ln(e!)
ln(Γ(e))
ln(e / (1 − e))
e1 if e1 > e2; e2 otherwise
min(e1, e2)
phi(e)
pow(e1, e2)
sin(e)
sqrt(e)
rank(v, s)
ranked(v, s)
round(e)
sd(v)
step(e)
e1 if e1 < e2; e2 otherwise
standard normal cdf
e1e2
sine(e)
e1/2
number of components of v less than or equal to vs
Σivi
n−1
n = dim(v)
the s th smallest component of v
nearest integer to e
standard deviation of components of v (n − 1 in denominator)
1 if e >= 0; 0 otherwise
Σivi
sum(v)
trunc(e)
greatest integer less than or equal to e
A link function can also be specified acting on the left hand side of a logical node e.g.
logit(mu[i]) <- beta0 + beta1 * z1[i] + beta2 * z2[i] + b[i]
The following functions can be used on the left hand side of logical nodes as link functions: log, logit,
cloglog, and probit (where probit(x) <- y is equivalent to x <- phi(y)).
It is important to keep in mind that logical nodes are included only for notational convenience − they
cannot be given data or initial values (except when using the data transformation facility described
below).
Deviance: A logical node called "deviance" is created automatically by WinBUGS: this stores −2 *
log(likelihood), where 'likelihood' is the conditional probability of all data nodes given their stochastic parent
nodes. This node can be monitored, and contributes to the DIC function − see DIC...
Arrays and indexing
[ top | home ]
Arrays are indexed by terms within square brackets. The four basic operators +, -, *, and / along with
appropriate bracketing are allowed to calculate an integer function as an index, for example:
Y[(i + j) * k, l]
On the left-hand-side of a relation, an expression that always evaluates to a fixed value is allowed for an index,
whether it is a constant or a function of data. On the right-hand-side the index can be a fixed value or a named
node, which allows a straightforward formulation for mixture models in which the appropriate element of
an array is 'picked' according to a random quantity (see Nested indexing and mixtures). However, functions of
unobserved nodes are not permitted to appear directly as an index term (intermediate deterministic nodes may
be introduced if such functions are required).
The conventions broadly follow those of S-Plus:
n:m
x[]
y[,3]
represents n, n + 1, ..., m.
represents all values of a vector x.
indicates all values of the third column of a two-dimensional array y.
Multidimensional arrays are handled as one-dimensional arrays with a constructed index. Thus functions
defined on arrays must be over equally spaced nodes within an array: for example sum(i, 1:4, k).
When dealing with unbalanced or hierarchical data a number of different approaches are possible − see
Handling unbalanced datasets. The ideas discussed in Nested indexing and mixtures may also be helpful in
this respect; the user should bear in mind, however, the 'contiguous elements' restriction described in
Constraints on using certain distributions.
Repeated structures
[ top | home ]
Repeated structures are specified using a "for-loop". The syntax for this is:
for (i in a:b) {
}
list of statements to be repeated for increasing values of loop-variable i
Note that neither
a nor b may be stochastic − see here for a possible way to get round this.
Data transformations
[ top | home ]
Although transformations of data can always be carried out before using WinBUGS, it is convenient to be able
to try various transformations of dependent variables within a model description. For example, we may wish to
try both y and sqrt(y) as dependent variables without creating a separate variable z = sqrt(y) in the data file.
The BUGS language therefore permits the following type of structure to occur:
for (i in 1:N) {
z[i] <- sqrt(y[i])
z[i] ~ dnorm(mu, tau)
}
Strictly speaking, this goes against the declarative structure of the model specification, with the accompanying
exhortation to construct a directed graph and then to make sure that each node appears once and only once
on the left-hand side of a statement. However, a check has been built in so that, when finding a logical node
which also features as a stochastic node (such as z above), a stochastic node is created with the calculated
values as fixed data.
We emphasise that this construction is only possible when transforming observed data (not a function of data
and parameters) with no missing values.
This construction is particularly useful in Cox modelling and other circumstances where fairly complex
functions of data need to be used. It is preferable for clarity to place the transformation statements in a section
at the beginning of the model specification, so that the essential model description can be examined
separately. See the Leuk and Endo examples.
Nested indexing and mixtures
[ top | home ]
Nested indexing can be very effective. For example, suppose N individuals can each be in one of I groups, and
g[1:N] is a vector which contains the group membership. Then "group" coefficients beta[i] can be fitted using
beta[g[j]] in a regression equation.
In the BUGS language, nested indexing can be used for the parameters of distributions: for example, the Eyes
example concerns a normal mixture in which the i th case is in an unknown group Ti which determines the
mean λT of the measurement yi . Hence the model is
i
Ti ~ Categorical(P)
yi ~ Normal(λT , τ )
i
which may be written in the BUGS language as
for (i in 1:N) {
T[i] ~ dcat(P[])
y[i] ~ dnorm(lambda[T[i]], tau)
}
However, when using Doodles the parameters of a distribution must be a node in the graph, and so an
additional stage is needed to specify the mean µ i = λT , as shown in the graph below. (We emphasise the
i
care required in establishing convergence of these notorious models.)
name:
mu[i]
value:
lambda[T[i]]
type:
logical
link:
identity
P[1:2]
lambda[k]
T[i]
for(k IN 1 : 2)
tau
mu[i]
y[i]
for(i IN 1 : N)
Vector parameters can also be identified dynamically, but currently only to a maximum of two dimensions. For
example, if we wanted a two-state categorical variable x to have a vector of probabilities indexed by i and j,
then we could write x ~ dcat(p[i, j, 1:2]). However, suppose we require three-level indexing, for
example
a
b
c
d
~
~
~
~
dcat(p.a[1:2])
dcat(p.b[1:2])
dcat(p.c[1:2])
dcat(p.d[a, b, c, 1:2]
WinBUGS will not permit this, and so the index must be explicitly calculated:
d ~ dcat(p[k, 1:2])
k <- 8 * (a - 1) + 4 * (b - 1) + c
This 'calculated index' trick is useful in many circumstances.
Formatting of data
[ top | home ]
Data can be S-Plus format (see most of the examples) or, for data in arrays, in rectangular format.
The whole array must be specified in the file - it is not possible just to specify selected components.
Missing values are represented as
NA.
All variables in a data file must be defined in a model, even if just left unattached to the rest of the model. In
Doodles such variables can be left as constants: in a model description they can be assigned vague priors or
allocated to dummy variables.
S-Plus format: This allows scalars and arrays to be named and given values in a single structure headed by
key-word list. There must be no space after list.
For example, in the Rats example, we need to specify a scalar xbar, dimensions N and T, a vector x and a twodimensional array Y with 30 rows and 5 columns. This is achieved using the following format:
list(
xbar = 22, N = 30, T = 5,
)
x = c(8.0, 15.0, 22.0, 29.0, 36.0),
Y = structure(
.Data = c(
151, 199, 246, 283, 320,
145, 199, 249, 293, 354,
...................
...................
137, 180, 219, 258, 291,
153, 200, 244, 286, 324),
.Dim = c(30, 5)
)
See the examples for other use of this format.
WinBUGS reads data into an array by filling the right-most index first, whereas the S-Plus program fills the leftmost index first. Hence WinBUGS reads the string of numbers c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) into a 2 * 5
dimensional matrix in the order
[i, j]th element of matrix
[1, 1]
[1, 2]
[1, 3]
.....
[1, 5]
[2, 1]
.....
[2, 5]
value
1
2
3
..
5
6
..
10
whereas S-Plus reads the same string of numbers in the order
[i, j]th element of matrix
[1, 1]
[2, 1]
[1, 2]
.....
[1, 3]
[2, 3]
.....
[2, 5]
value
1
2
3
..
5
6
..
10
Hence the ordering of the array dimensions must be reversed before using the S-Plus
create a data file for input into WinBUGS.
For example, consider the 2 * 5 dimensional matrix
1
6
2
7
3
8
4
9
5
10
This must be stored in S-Plus as a 5 * 2 dimensional matrix:
> M
[1,]
[2,]
[3,]
[4,]
[5,]
[,1] [,2]
1
6
2
7
3
8
4
9
5
10
The S-Plus command
dput command to
> dput(list(M=M), file="matrix.dat")
will then produce the following data file
list(M = structure(.Data = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
.Dim=c(5,2))
Edit the .Dim statement in this file from .Dim=c(5,2) to .Dim=c(2,5). The file is now in the correct
format to input the required 2 * 5 dimensional matrix into WinBUGS.
Now consider a 3 * 2 * 4 dimensional array
1
5
2
6
3
7
4
8
9
13
10
14
11
15
12
16
17
21
18
22
19
23
20
24
This must be stored in S-Plus as the 4 * 2 * 3 dimensional array:
> A
, , 1
[,1] [,2]
[1,]
1
5
[2,]
2
6
[3,]
3
7
[4,]
4
8
, , 2
[,1] [,2]
[1,]
9
13
[2,]
10
14
[3,]
11
15
[4,]
12
16
, , 3
[,1] [,2]
[1,]
17
21
[2,]
18
22
[3,]
19
23
[4,]
20
24
The command
> dput(list(A=A), file="array.dat")
will then produce the following data file
list(A = structure(.Data = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24), .Dim=c(4,2,3))
Edit the .Dim statement in this file from .Dim=c(4,2,3) to .Dim=c(3,2,4). The file is now in the
correct format to input the required 3 * 2 * 4 dimensional array into WinBUGS in the order
[i, j, k]th element of matrix
[1, 1, 1]
[1, 1, 2]
value
1
2
.....
[1, 1, 4]
[1, 2, 1]
[1, 2, 2]
.....
[2, 1, 3]
[2, 1, 4]
[2, 2, 1]
[2, 2, 2]
.....
[3, 2, 3]
[3, 2, 4]
..
4
5
6
..
11
12
13
14
..
23
24
Rectangular format: The columns for data in rectangular format need to be headed by the array name. The
arrays need to be of equal size, and the array names must have explicit brackets: for example:
age[]
sex[]
26
0
52
1
.....
34
0
END
Note that the file must end with an 'END' statement, as shown above and below, and this must be
followed by at least one blank line.
Multi-dimensional arrays can be specified by explicit indexing: for example, the Ratsy file begins
Y[,1]
151
145
147
Y[,2]
199
199
214
153
END
200
Y[,3]
246
249
263
.......
244
Y[,4]
283
293
312
Y[,5]
320
354
328
286
324
The first index position for any array must always be empty.
It is possible to load a mixture of rectangular and S-Plus format data files for the same model. For example, if
data arrays are provided in a rectangular file, constants can be defined in a separate list statement (see also
the Rats example with data files Ratsx and Ratsy).
(See here for details of how to handle unbalanced data.)
Note that programs exist for conversion of data from other packages: please see the BUGS resources webpage at http://www.mrc-bsu.cam.ac.uk/bugs/weblinks/webresource.shtml
DoodleBUGS: The Doodle Editor
Contents
General properties
Creating a node
Selecting a node
Deleting a node
Moving a node
Creating a plate
Selecting a plate
Deleting a plate
Moving a plate
Resizing a plate
Creating an edge
Deleting an edge
Moving a Doodle
Resizing a Doodle
Printing a Doodle
General properties
[ top | home ]
Doodles consist of three elements: nodes, plates and edges. The graph is built up out of these elements using
the mouse and keyboard, controlled from the Doodle menu. The menu options are described below.
New: opens a new window for drawing Doodles. A dialog box opens allowing a choice of size of the Doodle
graphic and the size of the nodes of the graph.
Grid: the snap grid is on the centre of each node and each corner of each plate is constrained to lie on the
grid.
Scale Model: shrinks the Doodle so that the size of each node and plate plus the separation between them is
reduced by a constant factor. The Doodle will move towards the top left corner of the window. This command is
useful if you run out of space while drawing the Doodle. Note that the Doodle will still be constrained by the
snap grid, and if the snap is coarse then the Doodle could be badly distorted.
Remove Selection: removes the highlighting from the selected node or plate of the Doodle if any.
Write Code: opens a window containing the BUGS language equivalent to the Doodle. No variable declaration
statement is produced as it is not needed within WinBUGS. After constructing a Doodle, you are strongly
recommended to use Write Code to check the structure is that which you intended.
Creating a node
[ top | home ]
Point the mouse cursor to an empty region of the Doodle window and click. A flashing caret appears next to
the blue word name. Typed characters will appear both at this caret and within the outline of the node.
Constant nodes can be given a name or a numerical value. The name of a node starts with a letter and can also
contain digits and the period character "." The name must not contain two successive periods and must not
end with a period. Vectors are denoted using a square bracket notation, with indices separated by commas. A
colon-separated pair of integers is used to denote an index range of a multivariate node or plate.
When first created the node will be of type stochastic and have associated density dnorm.
The type of the node can be changed by clicking on the blue word type at the top of the doodle. A menu will
drop down giving a choice of stochastic, logical and constant for the node type.
Stochastic: Associated with stochastic nodes is a density. Click on the blue word density to see the choice of
densities available (this will not necessarily include all those available in the BUGS language and described in
Distributions). For each density the appropriate name(s) of the parameters are displayed in blue. For some
densities default values of the parameters will be displayed in black next to the parameter name. When edges
are created pointing into a stochastic node these edges are associated with the parameters in a left to right
order. To change the association of edges with parameters click on one of the blue parameter names, a menu
will drop down from which the required edge can be selected. This drop down menu will also give the option of
editing the parameter's default value.
Logical: Associated with logical nodes is a link, which can be selected by clicking on the blue word link.
Logical nodes also require a value to be evaluated (modified by the chosen link) each time the value of the node
is required. To type input in the value field click the mouse on the blue word value. The value field of a logical
node corresponds to the right hand side of a logical relation in the BUGS language and can contain the same
functions. The value field must be completed for all logical nodes.
We emphasise that the value determines the value of the node and the logical links in the Doodle are for
cosmetic purposes only.
It is possible to define two nodes in the same Doodle with the same name − one as logical and one as
stochastic − in order to use the data transformation facility described in Data transformations.
Constants: These need to be specified in the data file. If they only appear in value statements, they do not
need to be explicitly represented in the Doodle.
Selecting a node
[ top | home ]
Point the mouse cursor inside the node and left-mouse-click.
Deleting a node
[ top | home ]
Select a node and press ctrl + delete key combination.
Moving a node
[ top | home ]
Select a node and then point the mouse into selected node. Hold mouse button down and drag node. The
cursor keys may also be used.
Creating a plate
[ top | home ]
Point the mouse cursor to an empty region of the Doodle window and click while holding the ctrl key down.
Selecting a plate
[ top | home ]
Point the mouse into the lower or right hand border of the plate and click.
Deleting a plate
[ top | home ]
Select a plate and press ctrl + delete key combination.
Moving a plate
[ top | home ]
Select a plate and then point the mouse into the lower or right hand border of the selected plate. Hold the
mouse button down and drag the plate.
Resizing a plate
[ top | home ]
Select a plate and then point the mouse into the small region at the lower right where the two borders intercept.
Hold the mouse button down and drag to resize the plate.
Creating an edge
[ top | home ]
Select node into which the edge should point and then click into its parent while holding down the ctrl key.
Deleting an edge
[ top | home ]
Select node whose incoming edge is to be deleted and then click into its parent while holding down the ctrl
key.
Moving a Doodle
[ top | home ]
After constructing a Doodle, it can be moved into a document that may also contain data, initial values, and
other text and graphics. This can be done by choosing Select Document from the Edit menu, and then either
copying and pasting, or dragging, the Doodle.
Resizing a Doodle
[ top | home ]
To change the size of Doodle which is already in a document containing text, click once into the Doodle with
the left mouse button. A narrow border with small solid squares at the corners and mid-sides will appear. Drag
one of these squares with the mouse until the Doodle is of the required size.
Printing a Doodle
[ top | home ]
If a Doodle is in its own document, it may be printed directly from the File menu. If a postscript version of a
Doodle is required, you could install a driver for a postscript printer (say Apple LaserWriter), but set it up to
print to file (checking the paper size is appropriate). Alternatively Doodles can be copied to a presentation or
word-processing package and printed from there.
The Model Menu
Contents
General properties
Specification...
Update...
Monitor Metropolis
Save State
Seed...
Script
General properties
[ top | home ]
The commands in this menu either apply to the whole statistical model or open dialog boxes. This menu is only
on display if the focus view is a text window or a Doodle.
Specification...
[ top | home ]
This non-modal dialog box acts on the focus view.
check model: If the focus view contains text, WinBUGS assumes the model is specified in the BUGS
language. The check model button parses the BUGS language description of the statistical model, as in the
classic version of BUGS. If a syntax error is detected the cursor is placed where the error was found and a
description of the error is given on the status line (lower left corner of screen). If a stretch of text is highlighted
the parsing starts from the first character highlighted (i.e. highlight the word model) else parsing starts at the
top of the window.
If the focus view contains a Doodle (i.e. the Doodle has been selected and is surrounded by a hairy border),
WinBUGS assumes the model has been specified graphically. If a syntax error is detected the node where the
error was found is highlighted and a description of the error is given on the status line.
load data: The load data button acts on the focus view; it will be greyed out unless the focus view contains
text.
Data can be identified in two ways:
1) if the data is in a separate document, the window containing that document needs to be in the focus view
(the windows title bar will be coloured not grey) when the load data command is used;
2) if the data is specified as part of a document, the first character of the data (either list if in S-Plus
format, or the first array name if in rectangular format) must be highlighted and the data will be read from
there on.
See here for details of data formats
Any syntax errors or data inconsistencies are displayed in the status line. Corrections can be made to the data
without returning to the check model stage. When the data have been loaded successfully, 'Data Loaded'
should appear in the status line.
The load data button becomes active once a model has been successfully checked, and ceases to be active
once the model has been successfully compiled.
num of chains: The number of chains to be simulated can be entered into the text entry field next to the
caption num of chains. This field can be typed in after the model has been checked and before the model has
been compiled. By default, one chain is simulated.
compile: The compile button builds the data structures needed to carry out Gibbs sampling. The model is
checked for completeness and consistency with the data.
A node called 'deviance' is automatically created which calculates minus twice the log-likelihood at each
iteration, up to a constant. This node can be monitored by typing deviance in the Samples dialog box.
This command becomes active once the model has been successfully checked, and when the model has been
successfully compiled, 'model compiled' should appear in the status line.
load inits: The load inits button acts on the focus view: it will be greyed out unless the focus view contains
text. The initial values will be loaded for the chain indicated in the text entry field to the right of the caption for
chain. The value of this text field can be edited to load initial values for any of the chains.
Initial values are specified in exactly the same way as data files. If some of the elements in an array are known
(say because they are constraints in a parameterisation), those elements should be specified as missing (NA)
in the initial values file.
This command becomes active once the model has been successfully compiled, and checks that initial values
are in the form of an S-Plus object or rectangular array and that they are consistent with any previously loaded
data. Any syntax errors or inconsistencies in the initial value are displayed.
If, after loading the initial values, the model is fully initialized this will be reported by displaying the message
initial values loaded: model initialized. Otherwise the status line will show the message initial values loaded:
model contains uninitialized nodes. The second message can have several meanings:
a) If only one chain is simulated it means that the chain contains some nodes that have not been initialized
yet.
b) If several chains are to be simulated it could mean that no initial values have been loaded for one of the
chains.
In either case further initial values can be loaded, or the gen inits button can be pressed to try and generate
initial values for all the uninitialized nodes in all the simulated chains.
Generally it is recommended to load initial values for all fixed effect nodes (founder nodes with no parents) for
all chains, initial values for random effects can be generated using the gen inits button.
This load inits button can still be executed once Gibbs sampling has been started. It will have the effect of
starting the sampler out on a new trajectory. A modal warning message will appear if the command is used in
this context.
gen inits: The gen inits button attempts to generate initial values by sampling either from the prior or from an
approximation to the prior. In the case of discrete variables a check is made that a configuration of zero
probability is not generated. This command will generate extreme values if any of the priors are very vague. If
the command is successful the message 'initial values generated: model initialized' is displayed otherwise the
message 'could not generate initial values' is displayed.
The gen inits button becomes active once the model has been successfully compiled, and will cease to be
active once the model has been initialized.
Update...
[ top | home ]
This command will become active once the model has been compiled and initialized, and has fields:
updates: number of MCMC updates to be carried out.
refresh: the number of updates between redrawing the screen.
thin: the samples from every k th iteration will be stored, where k is the value of thin. Setting k > 1 can help
to reduce the autocorrelation in the sample, but there is no real advantage in thinning except to reduce
storage requirements and the cost of handling the simulations when very long runs are being carried out.
update: Click to start updating the model. Clicking on update during sampling will pause the simulation after
the current block of iterations, as defined by refresh, has been completed; the number of updates required can
then be changed if needed. Clicking on update again will restart the simulation. This button becomes active
when the model has been successfully compiled and given initial values.
iteration: shows the total number of iterations stored after thinning − not the actual number of iterations carried
out. In this respect updates represents the required number of samples rather than MCMC updates: for
example, if 100 samples are requested (via updates = 100) and thin is set equal to 10, then 10 * 100 = 1000
iterations will actually be carried out, of which 100 (every 10th) will be stored.
over relax: click on this box (a tick will then appear) to select an over-relaxed form of MCMC (Neal, 1998)
which will be executed where possible. This generates multiple samples at each iteration and then selects one
that is negatively correlated with the current value. The time per iteration will be increased, but the within-chain
correlations should be reduced and hence fewer iterations may be necessary. However, this method is not
always effective and should be used with caution. The auto-correlation function may be used to check whether
the mixing of the chain is improved.
adapting: This box will be ticked while the Metropolis or slice-sampling MCMC algorithm is in its initial tuning
phase where some optimization parameters are tuned. All summary statistics for the model will ignore
information from this adapting phase. The Metropolis and slice-sampling algorithms have adaptive phases of
4000 and 500 iterations respectively which will be discarded from all summary statistics. For details of how to
change these default settings please see Update options...
Monitor Metropolis
[ top | home ]
This command is only active if the Metropolis algorithm is being used for the model. This shows the minimum,
maximum and average acceptance rate averaged over 100 iterations as the Metropolis algorithm adapts over
the first N iterations. The rate should lie between the two horizontal lines. The first N iterations of the simulation
cannot be used for statistical inference. The default value of N is 4000 (see Update options... for details of how
to change this value).
Save State
[ top | home ]
Opens a window showing the current state of all the stochastic variables in the model, displayed in S-Plus
format. This could then be used as an initial value file for future runs.
Seed...
[ top | home ]
Opens a non-modal dialog box containing:
seed: a text entry field where the new seed of the random number generator can be typed.
coverage: the pseudo-random number generator used by WinBUGS generates a finite (albeit very long)
sequence of distinct numbers, which would eventually be repeated if the sampler were run for a sufficiently long
time. The coverage field shows the percentage of this sequence covered during the current WinBUGS session.
set: sets the seed to the value entered into the dialog box. The seed must be set after the model is checked
(via 'check model' − see Specification...) in order for the new value to apply to the current analysis.
Script
[ top | home ]
The Script command is used to run "model scripts" in batch-mode: if the focus-view contains a series of
WinBUGS batch-mode commands then selecting this command from the Model menu will cause the script to
be executed. Please see Batch-mode: Scripts for full details.
The Inference Menu
Contents
General properties
Samples...
Compare...
Correlations...
Summary...
Rank...
DIC...
General properties
[ top | home ]
These menu items open dialog boxes for making inferences about parameters of the model. The commands are
divided into three sections: the first three commands concern an entire set of monitored values for a variable;
the next two commands are space-saving short-cuts that monitor running statistics; and the final command,
DIC..., concerns evaluation of the Deviance Information Criterion proposed by Spiegelhalter et al. (2002). Users
should ensure their simulation has converged before using Summary..., Rank... or DIC... Note that if
the MCMC simulation has an adaptive phase it will not be possible to make inference using values sampled
before the end of this phase.
Samples...
[ top | home ]
This command opens a non-modal dialog for analysing stored samples of variables produced by the MCMC
simulation. The fields are:
node: The variable of interest must be typed in this text field. If the variable of interest is an array, slices of the
array can be selected using the notation variable[lower0:upper0, lower1:upper1, ...].
The buttons at the bottom of the dialog act on this variable. A star '*' can be entered in the node text field as
shorthand for all the stored samples.
WinBUGS generally automatically sets up a logical node to measure a quantity known as deviance; this may
be accessed, in the same way as any other variable of interest, by typing its name, i.e. "deviance", in the node
field of the Sample Monitor Tool. The definition of deviance is −2 * log(likelihood): 'likelihood' is defined as p(y|
theta), where y comprises all stochastic nodes given values (i.e. data), and theta comprises the stochastic
parents of y − 'stochastic parents' are the stochastic nodes upon which the distribution of y depends, when
collapsing over all logical relationships.
beg and end: numerical fields are used to select a subset of the stored sample for analysis.
thin: numerical field used to select every k th iteration of each chain to contribute to the statistics being
calculated, where k is the value of the field. Note the difference between this and the thinning facility on the
Update Tool dialog box: when thinning via the Update Tool we are permanently discarding samples as the
MCMC simulation runs, whereas here we have already generated (and stored) a suitable number of (posterior)
samples and may wish to discard some of them only temporarily. Thus, setting k > 1 here will not have any
impact on the storage (memory) requirements of processing long runs; if you wish to reduce the number of
samples actually stored (to free-up memory) you should thin via the Update Tool.
chains . to .: can be used to select the chains which contribute to the statistics being calculated.
clear: removes the stored values of the variable from computer memory.
set: must be used to start recording a chain of values for the variable.
trace: plots the variable value against iteration number. This trace is dynamic, being redrawn each time the
screen is redrawn.*
history: plots out a complete trace for the variable.*
The next six buttons will be greyed out if the MCMC simulation is in an adaptive phase.
density: plots a smoothed kernel density estimate for the variable if it is continuous or a histogram if it is
discrete.*
auto cor: plots the autocorrelation function of the variable out to lag-50. The values underlying these can be
listed to a window by double-clicking on the plot followed by ctrl-left-mouse-click: if multiple chains are being
simulated the values for each chain will be given.*
stats: produces summary statistics for the variable, pooling over the chains selected. The required percentiles
can be selected using the percentile selection box. The quantity reported in the MC error column gives an
estimate of σ / N1/2, the Monte Carlo standard error of the mean. The batch means method outlined by Roberts
(1996; p.50) is used to estimate
σ.
coda: dumps out an ascii representation of the monitored values suitable for use in the CODA S-Plus
diagnostic package. An output file for each chain is produced, corresponding to the .out files of CODA,
showing the iteration number and value (to four significant figures). There is also a file containing a description
of which lines of the .out file correspond to which variable − this corresponds to the CODA .ind file. These
can be named accordingly and saved as text files for further use. (Care may be required to stop the Windows
system adding a .txt extension when saving: enclosing the required file name in quotes should prevent this.)
quantiles: plots out the running mean with running 95% confidence intervals against iteration number.*
bgr diag: calculates the Gelman-Rubin convergence statistic, as modified by Brooks and Gelman (1998). The
width of the central 80% interval of the pooled runs is green, the average width of the 80% intervals within the
individual runs is blue, and their ratio R (= pooled / within) is red - for plotting purposes the pooled and within
interval widths are normalised to have an overall maximum of one. The statistics are calculated in bins of length
50: R would generally be expected to be greater than 1 if the starting values are suitably over-dispersed. Brooks
and Gelman (1998) emphasise that one should be concerned both with convergence of R to 1, and with
convergence of both the pooled and within interval widths to stability.
The values underlying these plots can be listed to a window by double-clicking on the plot followed by ctrl-leftmouse-click on the window.*
* See WinBUGS Graphics for details of how to customize these plots.
Compare...
[ top | home ]
Select Compare... from the Inference menu to open the Comparison Tool dialog box. This is designed to
facilitate comparison of elements of a vector of nodes (with respect to their posterior distributions).
node: defines the vector of nodes to be compared with each other. As the comparisons are with respect to
posterior distributions, node must be a monitored variable.
other: where appropriate, other defines a vector of reference points to be plotted alongside each element of
node (on the same scale), for example, other may be the observed data in a model fit plot (see below). The
elements of other may be either monitored variables, in which case the posterior mean is plotted, or they may
be observed/known.
axis: where appropriate, axis defines a set of values against which the elements of node (and other) should be
plotted. Each element of axis must be either known/observed or, alternatively, a monitored variable, in which
case the posterior mean is used.
Note: node, other, and axis should all have the same number of elements!
beg and end: are used to select the subset of stored samples from which the desired plot should be derived.
box plot: this command button produces a single plot in which the posterior distributions of all elements of
node are summarised side by side. For example,
box plot: beta
8.0
[2]
[9]
[11]
[3]
7.0
[26]
[8]
[17]
[6]
[1]
6.0
[25]
[14]
[5]
[7]
[4]
[19]
[12][13]
[10]
[21]
[30]
[20]
[16] [18]
[22] [24]
[23]
[27][28]
[29]
[15]
5.0
4.0
By default, the distributions are plotted in order of the corresponding variable's index in node and are also
labelled with that index. Boxes represent inter-quartile ranges and the solid black line at the (approximate)
centre of each box is the mean; the arms of each box extend to cover the central 95 per cent of the distribution
− their ends correspond, therefore, to the 2.5% and 97.5% quantiles. (Note that this representation differs
somewhat from the traditional.)
(The default value of the baseline shown on the plot is the global mean of the posterior means.)
There is a special "property editor" available for box plots, as indeed there is for all graphics generated via the
Comparison Tool. This can be used to interact with the plot and change the way in which it is displayed, for
example, it is possible to rank the distributions by their means or medians and/or plot them on a logarithmic
scale. For full details, please see Box plot.
caterpillar: a "caterpillar" plot is conceptually very similar to a box plot. The only significant differences are that
the inter-quartile ranges are not shown and the default scale axis is now the x-axis − each distribution is
summarised by a horizontal line representing the 95% interval and a dot to show where the mean is. (Again,
the default baseline − in red − is the global mean of the posterior means.) Due to their greater simplicity
caterpillar plots are typically preferred over box plots when the number of distributions to be compared is large.
See Caterpillar plot for details of how to change the properties of caterpillar plots.
model fit: the elements of node (and other if specified) are treated as a time-series, defined by (increasing
values of) the elements of axis. The posterior distribution of each element of node is summarised by the 2.5%,
50% and 97.5% quantiles. Each of these quantities is joined to its direct neighbours (as defined by axis) by
straight lines (solid red in the case of the median and dashed blue for the 95% posterior interval) to form a
piecewise linear curve − the 'model fit'. In cases where other is specified, its values are also plotted, using black
dots, against the corresponding values of axis, e.g.
model fit: eta[1,1:7]
150.0
100.0
50.0
0.0
0.0
500.0
1.00E+3
1500.0
2.00E+3
Where appropriate, either or both axes can be changed to a logarithmic scale via a property editor − see
Model fit plot.
scatterplot: by default, the posterior means of node are plotted (using blue dots) against the corresponding
values of axis and an exponentially weighted smoother is fitted. Numerous properties of a scatterplot can be
modified using the editor described in Scatterplot − for example, the smoothing parameter may be changed, or
the smoother may be replaced by a different type of line, or 95% posterior intervals for node may be displayed,
etc.
Correlations...
[ top | home ]
This non-modal dialog box is used to plot out the relationship between the simulated values of selected
variables, which must have been monitored.
nodes: scalars or arrays may be entered in each box, and all combinations of variables entered in the two
boxes are selected. If a single array is given, all pairwise correlations will be plotted.
scatter: produces a scatter plot of the individual simulated values.
matrix: produces a matrix summary of the cross-correlations.
print: opens a new window containing the coefficients for all possible correlations among the selected
variables.
The calculations may take some time.
Summary...
[ top | home ]
This non modal dialog box is used to calculate running means, standard deviations and quantiles. The
commands in this dialog are less powerful and general than those in the Sample Monitor Tool, but they also
require much less storage (an important consideration when many variables and/or long runs are of interest).
node: The variable of interest must be typed in this text field.
set: starts recording the running totals for node.
stats: displays the running means, standard deviations, and 2.5%, 50% (median) and 97.5% quantiles for node.
Note that these running quantiles are calculated via an approximate algorithm (see here for details)
and should therefore be used with caution.
means: displays the running means for node in a comma delimited form. This can be useful for passing the
results to other statistical or display packages.
clear: removes the running totals for node.
Rank...
[ top | home ]
This non-modal dialog box is used to store and display the ranks of the simulated values in an array.
node: the variable to be ranked must be typed in this text field (must be an array).
set: starts building running histograms to represent the rank of each component of node. An amount of storage
proportional to the square of the number of components of node is allocated. Even when node has thousands of
components this can require less storage than calculating the ranks explicitly in the model specification and
storing their samples, and it is also much quicker.
stats: summarises the distribution of the ranks of each component of the variable node. The quantiles
highlighted in the percentile selection box are displayed.
histogram: displays the empirical distribution of the simulated rank of each component of the variable node.
clear: removes the running histograms for node.
DIC...
[ top | home ]
The DIC Tool dialog box is used to evaluate the Deviance Information Criterion (DIC; Spiegelhalter et al., 2002)
and related statistics − these can be used to assess model complexity and compare different models. Most of
the examples packaged with WinBUGS contain an example of their usage.
It is important to note that DIC assumes the posterior mean to be a good estimate of the stochastic
parameters. If this is not so, say because of extreme skewness or even bimodality, then DIC may not
be appropriate. There are also circumstances, such as with mixture models, in which WinBUGS will
not permit the calculation of DIC and so the menu option is greyed out. Please see the WinBUGS 1.4
web-page for current restrictions:
http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml
set: starts calculating DIC and related statistics − the user should ensure that convergence has been achieved
before pressing set as all subsequent iterations will be used in the calculation.
clear: if a DIC calculation has been started (via set) this will clear it from memory, so that it may be restarted
later.
DIC: displays the calculated statistics, as described below; please see Spiegelhalter et al. (2002) for full
details; the section Tricks: Advanced Use of the BUGS Language also contains some comments on the use of
DIC.
The DIC button generates the following statistics:
Dbar: this is the posterior mean of the deviance, which is exactly the same as if the node 'deviance' had been
monitored (see here). This deviance is defined as −2 * log(likelihood): 'likelihood' is defined as p(y|theta), where
y comprises all stochastic nodes given values (i.e. data), and theta comprises the stochastic parents of y −
'stochastic parents' are the stochastic nodes upon which the distribution of y depends, when collapsing over all
logical relationships.
Dhat: this is a point estimate of the deviance (−2 * log(likelihood)) obtained by substituting in the posterior
means theta.bar of theta: thus Dhat = −2 * log(p(y|theta.bar)).
pD: this is 'the effective number of parameters', and is given by pD = Dbar − Dhat. Thus pD is the posterior
mean of the deviance minus the deviance of the posterior means.
DIC: this is the 'Deviance Information Criterion', and is given by DIC = Dbar + pD = Dhat + 2 ∗ pD. The model
with the smallest DIC is estimated to be the model that would best predict a replicate dataset of the same
structure as that currently observed.
The Info Menu
Contents
General properties
Open Log
Clear Log
Node info...
Components
General properties
[ top | home ]
This menu allows the user to obtain more information about how the software is working.
Open Log
[ top | home ]
This option opens a log window to which error and status information is written.
Clear Log
[ top | home ]
This option clears all the information displayed in the log window.
Node info...
[ top | home ]
This dialog box allows information about particular nodes in the model to be obtained.
node: the name of the node for which information is required should be typed here.
values: displays the current value of the node (for each chain) in the log window.
methods: displays the type of updater used to sample from the node (if appropriate) in the log window.
node: displays the type used to represent the node in the log window.
state: opens a trap window showing the current state of the internal data structures used to represent the node;
this is for debugging purposes only − it is of no practical use to the user!
Components
[ top | home ]
Displays all the components (dynamic link libraries) in use.
The Options Menu
Contents
Output options...
Blocking options...
Update options...
Output options...
[ top | home ]
window or log: if window is selected then a new window will be opened for each new piece of output (statistics,
traces, etc.); if log is selected then all output will be pasted into a single log file.
output precision: the number of significant digits in numerical output.
Blocking options...
[ top | home ]
If the box is checked then, where possible, WinBUGS will use the multivariate updating method described in
Gamerman (1997) to generate new values for blocks of fixed-effect parameters.
Update options...
[ top | home ]
Once a model has been compiled, the various updating algorithms required in order to perform the MCMC
simulation may be 'tuned' somewhat via the Updater options dialog box (select Update options... from the
Options menu):
methods: this "selection box" shows the system names of all updating methods required/selected for the
current problem. All other components of the Updater options dialog box (i.e. fields and command buttons)
pertain to the currently selected item in methods.
used for: this text field simply describes what type of node the currently selected method is generally used for.
Note: it is now possible to change the sampling methods for certain classes of distribution, although this is
delicate and should be done carefully − please see Changing MCMC Defaults (advanced users only) for details.
iterations: some updating algorithms entail iterative procedures that terminate when some relevant criterion is
satisfied. It is always possible, however, that within a given Gibbs iteration this criterion cannot be satisfied in
reasonable time. In such cases, rather than allow the computer to 'hang', it is preferable to specify a maximum
number of iterations allowed before an error message is generated. This maximum number of iterations is
displayed in the iterations field, which may be edited. In cases where iterative procedures are not required the
iterations field will be greyed-out.
adaptive phase: some updating methods, such as Metropolis-Hastings, have an adaptive phase during which
their internal parameters are tuned based on information gained from the chain(s) generated so far. All samples
generated during an adaptive phase should be discarded when drawing inferences but sometimes the default
adaptive phase is longer than necessary, meaning that the sampler is somewhat wasteful. (Alternatively, the
default adaptive phase may not be sufficiently long to allow proper tuning.) When the adaptive phase field is not
greyed-out (indicating that the currently selected method in methods requires tuning) it displays the length of
the adaptive phase in iterations (Gibbs cycles) − this may be edited by the user.
over-relaxation: many updating methods are capable of generating over-relaxed samples. Here, at each
iteration, a number of candidate samples is generated and one that is negatively correlated with the current
value is selected − the time per iteration will be increased but within-chain correlations should be reduced. The
number of candidate samples (including the current value) is displayed in the over-relaxation field.
Set: The Set command button applies the values shown in iterations, adaptive phase, and over-relaxation to the
updating method currently selected in methods for the current model − if a new model is loaded or if WinBUGS
is shut-down and re-started then the software will revert to its default values.
Save: The Save button also applies the values shown in iterations, adaptive phase, and over-relaxation to the
currently selected method, but it also saves those values as defaults for that method - the next time that that
method is used the new values will be selected automatically.
Batch-mode: Scripts
The Scripting Language
As an alternative to the menu/dialog box interface of WinBUGS a scripting language has been provided. This
language can be useful for automating routine analysis. The language works in effect by writing values into
fields and clicking on buttons in relevant dialog boxes. Note that it is possible to combine use of this scripting
language with use of the menu/dialog box interface.
To make use of the scripting language for a specific problem, a minimum of four files are required: the script
itself; a file containing the BUGS language representation of the model; a file (or several) containing the data;
and for each chain a file containing initial values. Each file may be in either native WinBUGS format (.odc) or
text format, in which case it must have a .txt extension.
The shortcut BackBUGS has been set up to run the commands contained in the file script.odc (in the root
directory of WinBUGS) when it is double-clicked. Thus a WinBUGS session may be embedded within any
software component that can execute the BackBUGS shortcut.
Below is a list of currently implemented commands in the scripting language. Alongside each is a brief
synopsis of its menu/dialog box equivalent: first we have the menu name and then the associated menu option;
then, an underlined name corresponds to a button in a dialog box, whereas a name without an underline
corresponds to a dialog box field set equal to the value of the quantity in italics that the => points to.
If the menu/dialog box equivalent of the specified script command would normally be greyed out, because of
inappropriate timing, for example, then the script command will not execute and an error message will be
produced instead.
script command
menu/dialog box equivalent
display(option)
Options −> Output options... −> option = "window" or "log"
check(model file)
Model −> Specification... −> check model
data(data file)
blockfe(option)
compile(chains)
inits(chain, inits file)
gen.inits()
Model −> Specification... −> load data
Options −> Blocking options... −> fixed effects => option
Model −> Specification... −> num of chains => chains + compile
Model −> Specification... −> for chain => chain + load inits
Model −> Specification... −> gen inits
update(iterations)
refresh(every)
over.relax(option)
thin.updater(thin)
Model −>
Model −>
Model −>
Model −>
beg(iter)
end(iter)
first(iter)
last(iter)
thin.samples(thin)
set(node)
clear(node)
stats(node)
density(node)
autoC(node)
trace(node)
history(node)
quantiles(node)
gr(node)
coda(node, file stem)1
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Inference −>
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
Samples...
set.summary(node)
stats.summary(node)
mean.summary(node)
clear.summary(node)
Inference −>
Inference −>
Inference −>
Inference −>
Summary...
Summary...
Summary...
Summary...
set.rank(node)
stats.rank(node)
hist.rank(node)
clear.rank(node)
Inference −>
Inference −>
Inference −>
Inference −>
Rank...
Rank...
Rank...
Rank...
dic.set()
dic.stats()
Inference −> DIC... −> set
Inference −> DIC... −> DIC
quit()
save(file)2
File −> Exit
File −> Save As... −> File name: => file + Save
script(file)3
Model −> Script
Update...
Update...
Update...
Update...
−>
−>
−>
−>
updates => iterations + update
refresh => every
over relax => option
thin => thin
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
−>
beg => iter
end => iter
chains => iter
to => iter
thin => thin
node => node +
node => node +
node => node +
node => node +
node => node +
node => node +
node => node +
node => node +
node => node +
node => node +
−>
−>
−>
−>
node =>
node =>
node =>
node =>
node =>
node =>
node =>
node =>
set
clear
stats
density
auto cor
trace
history
quantiles
bgr diag
coda
node +
node +
node +
node +
node +
node +
node +
node +
set
stats
mean
clear
set
stats
histogram
clear
1 If
the file stem is left blank the CODA output is written to windows (one for each chain plus one for the index).
If the file stem is not blank then the CODA output is written to separate text files, each starting with file stem.
2 If the file ends with ".txt" the log window is saved to a text file (with all graphics, fonts, etc., stripped out);
otherwise the log window is saved as is.
3 Runs the script stored in file.
Tricks: Advanced Use of the BUGS Language
Contents
Specifying a new sampling distribution
Specifying a new prior distribution
Specifying a discrete prior on a set of values
Using pD and DIC
Mixtures of models of different complexity
Where the size of a set is a random quantity
Assessing sensitivity to prior assumptions
Modelling unknown denominators
Handling unbalanced datasets
Learning about the parameters of a Dirichlet distribution
Use of the "cut" function
Specifying a new sampling distribution
[ top | home ]
Suppose we wish to use a sampling distribution that is not included in the list of standard distributions, in
which an observation x[i] contributes a likelihood term L[i]. We may use the "zeros trick": a Poisson(phi)
observation of zero has likelihood exp(-phi), so if our observed data is a set of 0's, and phi[i] is set to −log(L[i]),
we will obtain the correct likelihood contribution. (Note that phi[i] should always be > 0 as it is a Poisson mean,
and so we may need to add a suitable constant to ensure that it is positive.) This trick is illustrated by an
example new-sampling in which a normal likelihood is constructed (using the zeros trick) and compared to the
standard analysis.
C <- 10000
# this just has to be large enough to ensure all phi[i]'s > 0
for (i in 1:N) {
zeros[i] <- 0
phi[i] <- -log(L[i]) + C
zeros[i] ~ dpois(phi[i])
}
This trick allows arbitrary sampling distributions to be used, and is particularly suitable when, say, dealing with
truncated distributions.
A new observation x.pred can be predicted by specifying it as missing in the data-file and assigning it a uniform
prior, e.g.
x.pred ~ dflat()
# improper uniform prior on new x
However our example shows that this method can be very inefficient and give a very high MC error.
An alternative to using 'zeros' is the "ones trick", where the data is a set of 1's assumed to be the results of
Bernoulli trials with probabilities p[i]. By making each p[i] proportional to L[i] (i.e. by specifying a scaling
constant large enough to ensure all p[i]'s are < 1) the required likelihood term is provided.
C <- 10000
# this just has to be large enough to ensure all p[i]'s < 1
for (i in 1:N) {
ones[i] <- 1
p[i] <- L[i] / C
ones[i] ~ dbern(p[i])
}
Specifying a new prior distribution
[ top | home ]
If, for a parameter theta, say, we want to use a prior distribution that is not part of the standard set, then we
can use the 'zeros' trick (see above) at the prior level. A single zero Poisson observation (with mean phi =
phi(theta)) contributes a term exp(-phi) to the likelihood for theta; when this is combined with a 'flat' prior for
theta the correct prior distribution results.
zero <- 0
theta ~ dflat()
phi <- expression for −log(desired prior for theta)
zero ~ dpois(phi)
This is illustrated in new-prior by an example in which a normal prior is constructed using the zeros trick and
the results are compared to the standard formulation. It is important to note that this method produces high
auto-correlation, poor convergence and high MC error, so it is computationally slow and long runs are
necessary.
Specifying a discrete prior on a set of values
[ top | home ]
Suppose you want a parameter D to take one of a set of values, d[1], ..., d[K], say, with probabilities p[1], ...,
p[K]. Then specify the arrays d[1:K] and p[1:K] in a data-file (or in the model) and use:
M ~ dcat(p[])
D <- d[M]
# choose which element of d[] to use.
This is illustrated by an example t-df of learning about the degrees of freedom of a t-distribution.
If the discrete prior is put on too coarse a grid, then there may be numerical problems (crashes), unless good
initial values are provided, and very poor mixing. It is therefore advised to either use a continuous prior or a
discrete prior on a fine grid − see the t-df example.
Using pD and DIC
[ top | home ]
Here we make a number of observations regarding the use of DIC and pD − for a full discussion see
Spiegelhalter et al. (2002):
1) DIC is intended as a generalisation of Akaike's Information Criterion (AIC). For non-hierarchical models,
pD should be approximately the true number of parameters.
2) Slightly different values of Dhat (and hence pD and DIC) can be obtained depending on the
parameterisation used for the prior distribution. For example, consider the precision tau (1 / variance) of a
normal distribution. The two priors
tau ~ dgamma(0.001, 0.001) and
log.tau ~ dunif(-10, 10); log(tau) <- log.tau
are essentially identical but will give slightly different results for Dhat: for the first prior the stochastic
parent is tau and hence the posterior mean of tau is substituted in Dhat, while in the second
parameterisation the stochastic parent is log.tau and hence the posterior mean of log(tau)
is substituted in Dhat.
3) For sampling distributions that are log-concave in their stochastic parents, pD is guaranteed to be
positive (provided the simulation has converged). However, it is theoretically possible to get negative
values. We have obtained negative pD's in the following situations:
i) with non-log-concave likelihoods (e.g. Student-t distributions) when there is substantial conflict
between prior and data;
ii) when the posterior distribution for a parameter is symmetric and bimodal, and so the posterior
mean is a very poor summary statistic and gives a very large deviance.
4) No MC error is available on the DIC. MC error on Dbar can be obtained by monitoring deviance and is
generally quite small. The primary concern is to ensure convergence of Dbar − it is therefore worthwhile
checking the stability of Dbar over a long chain.
5) The minimum DIC estimates the model that will make the best short-term predictions, in the same spirit
as Akaike's criterion. However, if the difference in DIC is, say, less than 5, and the models make very
different inferences, then it could be misleading just to report the model with the lowest DIC.
6) DICs are comparable only over models with exactly the same observed data, but there is no need for
them to be nested.
7) DIC differs from Bayes factors and BIC in both form and aims.
8) Caution is advisable in the use of DIC until more experience has been gained. It is important to note
that the calculation of DIC will be disallowed for certain models. Please see the WinBUGS 1.4
web-page for details:
http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml
Mixtures of models of different complexity
[ top | home ]
Suppose we assume that each observation, or group of observations, is from one of a set of distributions, where
the members of the set have different complexity. For example, we may think data for each person's growth
curve comes from either a linear or quadratic line. We might think we would require 'reversible jump' techniques,
but this is not the case as we are really only considering a single mixture model as a sampling distribution.
Thus standard methods for setting up mixture distributions can be adopted, but with components having
different numbers of parameters.
The mixtures example illustrates how this is handled in WinBUGS, using a set of simulated data.
Naturally, the standard warnings about mixture distributions apply, in that convergence may be poor and careful
parameterisation may be necessary to avoid some of the components becoming empty.
Where the size of a set is a random quantity
[ top | home ]
Suppose the size of a set is a random quantity: this naturally occurs in 'changepoint' problems where
observations up to an unknown changepoint K come from one model, and after K come from another. Note that
we cannot use the construction
for (i in 1:K) {
y[i] ~ model 1
}
for (i in (K + 1):N) {
y[i] ~ model 2
}
since the index for a loop cannot be a random quantity. Instead we can use the step function to set up an
indicator as to which set each observation belongs to:
for (i in 1:N) {
ind[i] <- 1 + step(i - K - 0.01)
y[i] ~ model ind[i]
}
# will be 1 for all i <= K, 2 otherwise
This is illustrated in random-sets by the problem of adding up terms in a series of unknown length, and in
Stagnant by a changepoint problem.
Assessing sensitivity to prior assumptions
[ top | home ]
One way to do this is to repeat the analysis under different prior assumptions, but within the same simulation in
order to aid direct comparison of results. Assuming the consequences of K prior distributions are to be
compared:
a) replicate the dataset K times within the model code;
b) set up a loop to repeat the analysis for each prior, holding results in arrays;
c) compare results using the 'compare' facility.
The example prior-sensitivity explores six different suggestions for priors on the random-effects variance in a
meta-analysis.
Modelling unknown denominators
[ top | home ]
Suppose we have an unknown Binomial denominator for which we wish to express a prior distribution. It can be
given a Poisson prior but this makes it difficult to express a reasonably uniform distribution. Alternatively a
continuous distribution could be specified and then the 'round' function used. For example, suppose we are told
that a fair coin has come up heads 10 times − how many times has it been tossed?
model {
r <- 10
p <- 0.5
r ~ dbin(p, n)
n.cont ~ dunif(1, 100)
n <- round(n.cont)
}
node
n
n.cont
mean
21.08
21.08
sd
4.794
4.804
MC error
0.07906
0.07932
2.5%
13.0
13.31
median
21.0
20.6
97.5%
32.0
32.0
start
1001
1001
sample
5000
5000
Assuming a uniform prior for the number of tosses, we can be 95% sure that the coin has been tossed between
13 and 32 times. A discrete prior on the integers could also have been used in this context.
Handling unbalanced datasets
[ top | home ]
Suppose we observe the following data on three individuals:
Person 1: 13.2
Person 2: 12.3 , 14.1
Person 3: 11.0, 9.7, 10.3, 9.6
There are three different ways of entering such 'ragged' data into WinBUGS:
1. Fill-to-rectangular: Here the data is 'padded out' by explicitly including the missing data, i.e.
y[,1]
13.2
12.3
11.0
END
y[,2]
NA
14.1
9.7
y[,3]
NA
NA
10.3
y[,4]
NA
NA
9.6
or list(y = structure(.Data = c(13.2, NA, NA, NA, 12.3, 14.1, NA, NA, 11.0, 9.7, 10.3, 9.6), .Dim = c(3, 4)).
A model such as y[i, j] ~ dnorm(mu[i],
one explicitly wishes to estimate the missing data.
1) can then be fitted. This approach is inefficient unless
2. Nested indexing: Here the data are stored in a single array and the associated person is recorded as a
factor, i.e.
y[]
13.2
12.3
14.1
11.0
9.7
10.3
9.6
END
person[]
1
2
2
3
3
3
3
or list(y = c(13.2, 12.3, 14.1, 11.0, 9.7, 10.3, 9.6), person = c(1, 2, 2, 3, 3, 3, 3)).
A model such as
y[k] ~ dnorm(mu[person[k]], 1) can then be fitted. This seems an efficient and
clear way to handle the problem.
3. Offset: Here an 'offset' array holds the position in the data array at which each person's data starts. For
example, the data might be
list(y = c(13.2, 12.3, 14.1, 11.0, 9.7, 10.3, 9.6), offset = c(1, 2, 4, 8))
and lead to a model containing the code
for (k in offset[i]:(offset[i + 1] - 1)) {
y[k] ~ dnorm(mu[i], 1)
}
The danger with this method is that it relies on getting the offsets correct and they are difficult to check. See
the ragged example for a full worked example of these methods.
Learning about the parameters of a Dirichlet distribution
[ top | home ]
Suppose as part of a model there are J probability arrays p[j, 1:K], j = 1, ..., J, where K is the dimension of
each array and sum(p[j, 1:K]) = 1 for all j. We give each of them a Dirichlet prior:
p[j, 1:K] ~ ddirch(alpha[])
and we would like to learn about alpha[]. However, the parameters alpha[] of a Dirichlet distribution cannot be
stochastic nodes. The trick is to note that if delta[k] ~ dgamma(alpha[k], 1), then the vector with elements
delta[k] / sum(delta[1:K]), k = 1, ..., K, is Dirichlet with parameters alpha[k], k = 1, ..., K. So the following
construction should allow learning about the parameters alpha[]:
for (k in 1:K) {
p[j, k] <- delta[j, k] / sum(delta[j,])
delta[j, k] ~ dgamma(alpha[k], 1)
}
A prior can be put directly on the alpha[k]'s.
Use of the "cut" function
[ top | home ]
Suppose we observe some data that we do not wish to contribute to the parameter estimation and yet we wish
to consider as part of the model. This might happen, for example:
a) when we wish to make predictions on some individuals on whom we have observed some partial data
that we do not wish to use for parameter estimation;
b) when we want to use data to learn about some parameters and not others;
c) when we want evidence from one part of a model to form a prior distribution for a second part of the
model, but we do not want 'feedback' from this second part.
The "cut" function forms a kind of 'valve' in the graph: prior information is allowed to flow 'downwards' through the
cut, but likelihood information is prevented from flowing upwards.
For example, the following code leaves the distribution for theta unchanged by the observation y.
model {
y <- 2
y ~ dnorm(theta.cut, 1)
theta.cut <- cut(theta)
theta ~ dnorm(0, 1)
}
WinBUGS Graphics
Contents
General properties
Margins
Axis Bounds
Titles
All Plots
Fonts
Specific properties (via Special...)
Density plot
Box plot
Caterpillar plot
Model fit plot
Scatterplot
General properties
[ top | home ]
All WinBUGS graphics have a set of basic properties that can be modified using the Plot Properties dialog box.
This is opened as follows: first, focus the relevant plot by left-clicking on it; then select Object Properties...
from the Edit menu. Alternatively, right-clicking on a focused plot will reveal a pop-up menu from which
Properties... may equivalently be selected. The Plot Properties dialogue box comprises a "tab-view" and two
command buttons, namely Apply and Special.... The tab-view contains five tabs that allow the user to make
different types of modification − these are discussed below. The Apply command button applies the properties
displayed in the currently selected tab to the focused plot. And Special... opens any special property editors
that have been designed so that the user may further interact with specific types of plot − these are discussed
below.
Margins
[ top | home ]
The Margins tab displays the plot's left, right, top and bottom margins in millimetres (mm). The left and bottom
margins are used for drawing the y- and x-axes respectively. The top margin provides room for the plot's title
and the right margin is typically used for plots that require a legend. (Note that top margins (and hence titles)
are always inside the plotting rectangle, i.e. there is no gap between the plotting rectangle and the top edge of
the graphic.)
In cases where it is not inappropriate to alter a plot's margins the user may enter his/her preferred values and
click on Apply to effect the desired change. If the specified values are not appropriate, e.g. if left + right is
greater than the width of the graphic (which would result in a plotting rectangle of negative width) or if any
margin is negative, etc., then either nothing will happen and the Margins tab will reset itself or some form of
compromise will be made.
Axis Bounds
[ top | home ]
The user may specify new minimum and maximum values for either or both axes using the Axis Bounds tab
(followed by the Apply button).
Note that the resulting minima and maxima may not be exactly the same as the values specified because
WinBUGS always tries to ensure that axes range between 'nice' numbers and also have a sufficient number of
tick marks. Note also that if max < min is specified then WinBUGS will ignore it and the Axis Bounds tab will
reset itself, but there is no guard against specifying a range that does not coincide with the data being plotted,
and so the contents of the plot may disappear!
(Some types of plot, such as trace plots, do not allow the user to change their axis bounds, because it would
be inappropriate to do so.)
Titles
[ top | home ]
The Titles tab should be self-explanatory.
Note, however, that because WinBUGS does not (yet) support vertical text, a substantial amount of space may
be required in the left margin in order to write the y-axis title horizontally − if sufficient space is not available
then the y-axis title may not appear at all (clearly, this can be rectified by increasing the left margin).
All Plots
[ top | home ]
The idea behind the All Plots tab is to allow the user to apply some or all of the properties of the focused plot to
all plots of the same type (as the focused plot) in the same window (as the focused plot).
The user should first configure the focused plot in the desired way and then decide which of the plot's various
properties are to be applied to all plots (of the same type, in the same window). The user should then check all
relevant check-boxes and click on Apply all. Be careful! It is easy to make mistakes here and there is no undo
option − the best advice for users who go wrong is to reproduce the relevant graphics window via the Sample
Monitor Tool or the Comparison Tool, for example.
Fonts
[ top | home ]
The Fonts tab allows the user to change the font of the plot's title, its axes, and any other text that may be
present.
First select the font to be modified and click on Modify.... (Note that it will only be possible to select other if the
focused plot has a third font, i.e. if text other than the title and axes is present on the plot, e.g. the labels on a
box plot − see Compare...) The self-explanatory Font dialogue box should appear − select the required font and
click on OK (or Cancel).
In order to apply the same font to an arbitrarily large group of plots (not necessarily of the same type), rather
than using the All Plots tab we use a "drag-and-pick" facility. First focus a single plot and select which font is
to be modified for the whole group: title, axes, or other (if available). Now highlight the group of plots using the
mouse. Hold down the ALT key and then the left-hand mouse button and drag the mouse over an area of text
with the desired font; then release the mouse button and the ALT key in turn and the required changes should
be made. As an alternative to dragging the mouse over a piece of text with the desired font, the user may
instead drag over another plot (even one in the group to be modified) − the group will adopt that plot's properties
for the selected font on the Fonts tab.
Specific properties (via Special...)
[ top | home ]
Below we describe the special property editors that are available for certain types of plot − these allow userinteraction beyond that afforded by the Plot Properties dialogue box and are accessed via its Special... button.
Density plot
[ top | home ]
When the density button on the Sample Monitor Tool is pressed, the output depends on whether the specified
variable is discrete or continuous − if the variable is discrete then a histogram is produced whereas if it is
continuous a kernel density estimate is produced instead. The specialized property editor that appears when
Special... on the Plot Properties dialogue box is selected also differs slightly depending on the nature of the
specified variable. In both cases the editor comprises a numeric field and two command buttons (apply and
apply all) but in the case of a histogram the numeric field corresponds to the "bin-size" whereas for kernel
density estimates it is the smoothing parameter* (see below):
property editor for histogram
property editor for kernel density estimate
In either case, the apply button sets the bin-size or smoothing parameter of the focused plot to the value currently
displayed in the numeric field. The apply all button, on the other hand, applies the value currently displayed in
the numeric field to all plots of the same type (as the focused plot) in the same window (as the focused plot).
Note*: We define the smoothing parameter for kernel density estimates, σ, via the definition of band-width.
Suppose our posterior sample comprises m realisations of variable z − denote these by z i, i = 1,..., m:
band-width = V1/2 / mσ ;
Σizi2 − (m−1Σizi)2
V = m−1
where the summations are from i = 1 to i = m. The default setting for σ is 0.2.
Box plot
[ top | home ]
The box plot property editor, opened by pressing Special... on the Plot Properties dialogue box when a box plot
is focused, is described as follows:
show baseline: this check-box should be used to specify whether or not a baseline should be shown on the
plot; the numeric field to the immediate right of the check-box gives the value of that baseline. The default
setting is that a baseline equal to the global mean of the posterior means will be shown − the user may specify
an alternative baseline simply by editing the displayed value.
show labels: check-box that determines whether or not each distribution/box should be labelled with its index
in node (that is node on the Comparison Tool). The default setting is that labels should be shown.
show means or show medians: these radio buttons specify whether the solid black line at the approximate
centre of each box is to represent the posterior mean or the posterior median − mean is the default.
rank: use this check-box to specify whether the distributions should be ranked and plotted in order. The basis
for ranking is either the posterior mean or the posterior median, depending on which is chosen to be displayed
in the plot (via show means or show medians).
vert. box or horiz. box: these radio buttons determine the orientation of the plot. The default is "vertical
boxes", which means that the scale axis (i.e. that which measures the 'width' of the distributions) is the y-axis.
log-scale: the scale axis can be given a logarithmic scale by checking this check-box.
Finally, in the bottom right-hand corner there is a colour field for selecting the fill-colour of the displayed boxes.
Caterpillar plot
[ top | home ]
The caterpillar plot property editor is virtually identical with the box plot property editor except that there is no
fill-colour field on the former:
Model fit plot
[ top | home ]
In cases where an axis is defined on a strictly positive range, it may be given a logarithmic scale by checking
the appropriate check-box:
Scatterplot
[ top | home ]
With a scatterplot focused, select Special... on the Plot Properties dialogue box to obtain the following property
editor:
show means or show medians: these radio buttons determine whether it is the posterior means or medians of
node that are plotted/scattered against axis to form the plot (that is node and axis on the Comparison Tool) −
the default is means.
Immediately beneath the show means or show medians radio buttons is a colour field for selecting the colour
of the scattered points.
log-x-/log-y-scale: either or both axes may be given a logarithmic scale (assuming they are defined on strictly
positive ranges) by checking the appropriate check-box(es).
show bars: 95 per cent posterior intervals (2.5% − 97.5%) for node will be shown on the plot (as vertical bars) if
this box is checked.
show line: use this check-box to specify whether or not a reference line (either a straight line specified via its
intercept and gradient or an exponentially weighted smoother − see below) is to be displayed.
To the immediate right of the show line check-box is a colour field that determines the reference line's colour (if
displayed).
linear or smooth: these radio buttons are used to specify whether the reference line (if it is to be displayed)
should be linear or whether an exponentially weighted smoother should be fitted instead. In the case where a
linear line is required, its intercept and gradient should be entered in the two numeric fields to the right of the
linear radio button (in that order). If a smoother is required instead then the desired degree of smoothing* (see
below) should be entered in the numeric field to the right of the smooth radio button. To save unnecessary
redrawing of the plot as the various numeric parameters are changed, the Redraw line command button is used
to inform WinBUGS of when all alterations to the parameters have been completed.
Note*: The exponentially weighted smoother that WinBUGS uses on scatterplots is defined as:
si =
Σjwijyj / Σjwij
i = 1,..., n
where n is the number of scattered points and the summations are from j = 1 to j = n. The weights wij are given
by
wij = exp(−|xi − x j | /
σ)
where σ is the smoothing parameter defined in the Scatterplot properties dialogue box (next to the smooth
radio button). The default setting for σ is (somewhat arbitrarily) {max(x) − min(x)} / 20.
Tips and Troubleshooting
Contents
Restrictions when modelling
Some error messages
Some Trap messages
The program hangs
Speeding up sampling
Improving convergence
This section covers a range of problems that might arise:
1) restrictions in setting up the model;
2) error messages generated when setting up the model or when sampling;
3) Traps: these corresponds to an error which has not been picked up by WinBUGS. It is difficult to
interpret the Trap message!
4) the program just hanging;
5) slow sampling;
6) lack of convergence.
Restrictions when modelling
[ top | home ]
Restrictions have been stated throughout this manual. A summary list is as follows:
a) Each stochastic and logical node must appear once and only once on the left-hand-side of an
expression. The only exception is when carrying out a data transformation (see Data transformations).
This means, for example, that it is generally not possible to give a distribution to a quantity that is
specified as a logical function of an unknown parameter.
b) Truncated sampling distributions cannot be handled using the I(.,.) construct − see
Censoring and truncation.
c) Multivariate distributions: Dirichlet and Wishart distributions may only be used as conjugate priors and
must have known parameters (although there does exist a trick for allowing unknown Dirichlet
parameters); in contrast, multinomial distributions may not be used as priors and the order N of the
distribution must be known; multivariate normal and Student-t distributions can be used anywhere in the
graph (i.e. as prior or likelihood) and there are no restrictions regarding their parameters. See
Constraints on using certain distributions.
d) Logical nodes cannot be given data or initial values. This means, for example, that it is not possible to
model observed data that is the sum of two random variables. (See Logical nodes.)
e) Poisson priors can only be specified for the (unknown) order (n) of a single binomial observation. In this
case, the sampling method for a 'shifted Poisson' distribution will be used (see MCMC methods). If more
than one binomial response is assumed to share the same order parameter, then the algorithm for a
shifted Poisson cannot be used to update the order parameter and WinBUGS will return the error
message "Unable to choose update method for n". A suitably constrained continuous prior may be used
for the order parameter, provided that it is used in conjunction with the round(.) function to ensure
integer values (see here for an example of using the 'round' function to this end).
Some error messages
[ top | home ]
a) 'expected variable name' indicates an inappropriate variable name.
b) 'linear predictor in probit regression too large' indicates numerical overflow. See possible solutions
below for Trap 'undefined real result'.
c) 'logical expression too complex' - a logical node is defined in terms of too many
parameters/constants or too many operators: try introducing further logical nodes to represent parts of
the overall calculation; for example, a1 + a2 + a3 + b1 + b2 + b3 could be written as A + B where A and
B are the simpler logical expressions a1 + a2 + a3 and b1 + b2 + b3, respectively. Note that linear
predictors with many terms should be formulated by 'vectorizing' parameters and covariates and by then
using the inprod(.,.) function (see Logical nodes).
d) 'invalid or unexpected token scanned' - check that the value field of a logical node in a Doodle has
been completed.
e) 'unable to choose update method' indicates that a restriction in the program has been violated − see
Restrictions when modelling above.
f) 'undefined variable' - variables in a data file must be defined in a model (just put them in as constants
or with vague priors). If a logical node is reported undefined, the problem may be with a node on the 'right
hand side'.
g) 'index out of range' - usually indicates that a loop-index goes beyond the size of a vector (or matrix
dimension); sometimes, however, appears if the # has been omitted from the beginning of a comment
line.
Some Trap messages
[ top | home ]
a) 'undefined real result' indicates numerical overflow. Possible reasons include:
- initial values generated from a 'vague' prior distribution may be numerically extreme - specify
appropriate initial values;
- numerically impossible values such as log of a non-positive number - check, for example, that no
zero expectations have been given when Poisson modelling;
- numerical difficulties in sampling. Possible solutions include:
- better initial values;
- more informative priors - uniform priors might still be used but with their range restricted to
plausible values;
- better parameterisation to improve orthogonality;
- standardisation of covariates to have mean 0 and standard deviation 1.
- can happen if all initial values are equal.
Probit models are particularly susceptible to this problem, i.e. generating undefined real results. If a
probit is a stochastic node, it may help to put reasonable bounds on its distribution, e.g.
probit(p[i]) <- delta[i]
delta[i] ~ dnorm(mu[i], tau)I(-5, 5)
This trap can sometimes be escaped from by simply clicking on the update button. The equivalent
construction
p[i] <- phi(delta[i])
may be more forgiving.
b) 'index array out of range' - possible reasons include:
- attempting to assign values beyond the declared length of an array;
- if a logical expression is too long to evaluate - break it down into smaller components.
c) 'stack overflow' can occur if there is a recursive definition of a logical node.
d) 'NIL dereference (read)' can occur at compilation in some circumstances when an inappropriate
transformation is made, for example an array into a scalar.
e) Trap messages referring to 'DFreeARS' indicate numerical problems with the derivative-free adaptive
rejection algorithm used for log-concave distributions. One possibility is to change to "Slice" sampling −
see here for details.
The program hangs
[ top | home ]
This could be due to:
a) a problem that seems to happen with NT - rebooting is a crude way out;
b) interference with other programs running - try to run WinBUGS on its own;
c) a particularly ill-posed model - try the approaches listed above under Trap messages.
Speeding up sampling
[ top | home ]
The key is to reduce function evaluations by expressing the model in as concise a form as possible. For
example, take advantage of the functions provided and use nested indexing wherever possible. Look at the
packaged examples and others provided on users' web sites.
Improving convergence
[ top | home ]
Possible solutions include:
a) better parameterisation to improve orthogonality of joint posterior;
b) standardisation of covariates to have mean 0 and standard deviation 1;
c) use of ordered over-relaxation.
For other problems, try the FAQ pages of the web site and the Classic BUGS manual. Then try mailing us on
[email protected]
Please try and keep the discussion list for modelling issues and problems other than my program won't run!
Tutorial
Contents
Introduction
Specifying a model in the BUGS language
Running a model in WinBUGS
Monitoring parameter values
Checking convergence
How many iterations after convergence?
Obtaining summaries of the posterior distribution
Introduction
[ top | home ]
This tutorial is designed to provide new users with a step-by-step guide to running an analysis in WinBUGS. It
is not intended to be prescriptive, but rather to introduce you to the main tools needed to run an MCMC
simulation in WinBUGS, and give some guidance on appropriate usage of the software.
The Seeds example from Volume I of the WinBUGS examples will be used throughout this tutorial. This
example is taken from Table 3 of Crowder (1978) and concerns the proportion of seeds that germinated on each
of 21 plates arranged according to a 2 by 2 factorial layout by seed and type of root extract. The data are
shown below, where ri and ni are the number of germinated and the total number of seeds on the ith plate, i = 1,
..., N. These data are also analysed by, for example, Breslow and Clayton (1993).
seed O. aegyptiaco 75
Bean
Cucumber
seed O. aegyptiaco 73
Bean
Cucumber
r
n
r/n
r
n
r/n
r
n
r/n
r
n
r/n
_____________________________________________________________________
10
23
23
26
17
39
62
81
51
39
0.26
0.37
0.28
0.51
0.44
5
53
55
32
46
10
6
74
72
51
79
13
0.83
0.72
0.76
0.63
0.58
0.77
8
10
8
23
0
16
30
28
45
4
0.50
0.33
0.29
0.51
0.00
3
22
15
32
3
12
41
30
51
7
0.25
0.54
0.50
0.63
0.43
The model is essentially a random effects logistic regression, allowing for over-dispersion. If pi is the probability
of germination on the ith plate, we assume
ri ~ Binomial(pi, ni)
logit(pi) = α 0 + α 1x1i + α 2x2i + α 12x1ix2i + bi
bi ~ Normal(0, τ)
where x 1i and x 2i are the seed type and root extract of the ith plate, and an interaction term α12x 1ix 2i is
included.
Specifying a model in the BUGS language
[ top | home ]
The BUGS language allows a concise expression of the model, using the 'twiddles' symbol ~ to denote
stochastic (probabilistic) relationships, and the left arrow ('<' sign followed by '−' sign) to denote deterministic
(logical) relationships. The stochastic parameters α0, α1, α2, α12, and τ are given proper but minimally
informative prior distributions, while the logical expression for sigma allows the standard deviation (of the
random effects distribution) to be estimated.
model {
for (i in 1:N) {
r[i] ~ dbin(p[i], n[i])
b[i] ~ dnorm(0, tau)
logit(p[i]) <- alpha0 + alpha1 * x1[i] + alpha2 * x2[i]
+ alpha12 * x1[i] * x2[i] + b[i]
}
alpha0 ~ dnorm(0, 1.0E-6)
alpha1 ~ dnorm(0, 1.0E-6)
alpha2 ~ dnorm(0, 1.0E-6)
alpha12 ~ dnorm(0, 1.0E-6)
tau ~ dgamma(0.001, 0.001)
sigma <- 1 / sqrt(tau)
}
More detailed descriptions of the BUGS language along with lists of the available logical functions and
stochastic distributions can be found in Model Specification and Distributions. See also the on-line examples:
Volume 1 and Volume 2. An alternative way of specifying a model in WinBUGS is to use the graphical interface
known as DoodleBUGS.
Running a model in WinBUGS
[ top | home ]
The WinBUGS software uses compound documents, which comprise various different types of information
(formatted text, tables, formulae, plots, graphs, etc.) displayed in a single window and stored in a single file.
This means that it is possible to run the model for the "seeds" example directly from this tutorial document,
since the model code can be made 'live' just by highlighting it. However, it is more usual when creating your
own models to have the model code and data etc. in separate files. We have therefore created a separate file
with the model code in it for this tutorial − you will find the file in Manuals/Tutorial/seeds_model.odc (or click
here).
Step 1
Open the seeds_model file as follows:
* Point to
on the tool bar and click once with the left mouse button (LMB).
* Highlight the
option and click once with LMB.
* Select the appropriate directory and double-click on the file to open.
Step 2
To run the model, we first need to check that the model description does fully define a probability model:
* Point to
* Highlight the
on the tool bar and click once with LMB.
option and click once with LMB.
* Focus the window containing the model code by clicking the LMB once anywhere in the window − the top
panel of the window should then become highlighted in blue (usually) to indicate that the window is
currently in focus.
* Highlight the word model at the beginning of the code by dragging the mouse over the word whilst
holding down the LMB.
* Check the model syntax by clicking once with LMB on the
button in the
window. A message saying "model is syntactically correct" should appear in the bottom left of the
WinBUGS program window.
Step 3
We next need to load in the data. The data can be represented using S-Plus object notation (file
Manuals/Tutorial/seeds_S_data.odc), or as a combination of an S-Plus object and a rectangular array with
labels at the head of each column (file Manuals/Tutorial/seeds_mix_data.odc).
* Open one of the data files now.
* To load the data in file Manuals/Tutorial/seeds_S_data.odc:
- Highlight the word list at the beginning of the data file.
- Click once with the LMB on the
button in the
window. A message
saying "data loaded" should appear in the bottom left of the WinBUGS program window.
* To load the data in file Manuals/Tutorial/seeds_mix_data.odc:
- Highlight the word list at the beginning of the data file.
- Click once with the LMB on the
button in the
window. A message
saying "data loaded" should appear in the bottom left of the WinBUGS program window.
- Next highlight the whole of the header line (i.e. column labels) of the rectangular array data.
button in the
window. A message
- Click once with the LMB on the
saying "data loaded" should appear in the bottom left of the WinBUGS program window.
Step 4
Now we need to select the number of chains (i.e. sets of samples to simulate). The default is 1, but we will use
2 chains for this tutorial, since running multiple chains is one way to check the convergence of your MCMC
simulations.
* Type the number 2 in the white box labelled
in the
window. In practice,
if you have a fairly complex model, you may wish to do a pilot run using a single chain to check that the
model compiles and runs and obtain an estimate of the time taken per iteration. Once you are happy with
the model, re-run it using multiple chains (say 2−5 chains) to obtain a final set of posterior estimates.
Step 5
button in the
Next compile the model by clicking once with the LMB on the
window. A message saying "model compiled" should appear in the bottom left of the WinBUGS program
window. This sets up the internal data structures and chooses the specific MCMC updating algorithms to be
used by WinBUGS for your particular model.
Step 6
Finally the MCMC sampler must be given some initial values for each stochastic node. These can be arbitrary
values, although in practice, convergence can be poor if wildly inappropriate values are chosen. You will need a
different set of initial values for each chain, i.e. two sets are needed for this tutorial since we have specified two
chains − these are stored in file Manuals/Tutorial/seeds_inits.odc.
* Open this file now.
* To load the initial values:
- Highlight the word list at the beginning of the first set of initial values.
- Click once with the LMB on the
button in the
window. A message
saying "initial values loaded: model contains uninitialized nodes (try running gen inits or loading more
files)" should appear in the bottom left of the WinBUGS program window.
- Repeat this process for the second initial values file. A message saying "initial values loaded: model
initialized" should now appear in the bottom left of the WinBUGS program window.
Note that you do not need to provide a list of initial values for every parameter in your model. You can get
WinBUGS to generate initial values for any stochastic parameter not already initialized by clicking with the
LMB on the
button in the
window. WinBUGS generates initial values by forward
sampling from the prior distribution for each parameter. Therefore, you are advised to provide your own initial
values for parameters with vague prior distributions to avoid wildly inappropriate values.
Step 7
Close the
window. You are now ready to start running the simulation. However, before
doing so, you will probably want to set some monitors to store the sampled values for selected parameters. For
the seeds example, set monitors for the parameters alpha0, alpha1, alpha2, alpha12 and sigma −
see here for details on how to do this.
To run the simulation:
* Select the
option from the
menu.
* Type the number of updates (iterations of the simulation) you require in the appropriate white box (labelled
) − the default value is 1000.
* Click once on the
button: the program will now start simulating values for each parameter in the
model. This may take a few seconds − the box marked
will tell you how many updates have
currently been completed. The number of times this value is revised depends on the value you have set
for the
option in the white box above the
box. The default is every 100 iterations, but
you can ask the program to report more frequently by changing
to, say, 10 or 1. A sensible
choice will depend on how quickly the program runs. For the seeds example, experiment with changing
the refresh option from 100 to 10 and then 1.
* When the updates are finished, the message "updates took *** s" will appear in the bottom left of the
WinBUGS program window (where *** is the number of seconds taken to complete the simulation).
* If you previously set monitors for any parameters you can now check convergence and view graphical and
numerical summaries of the samples. Do this now for the parameters you monitored in the seeds
example − see Checking convergence for tips on how to do this (for the seeds example, you should find
that at least 2000 iterations are required for convergence).
* Once you're happy that your simulation has converged, you will need to run some further updates to
obtain a sample from the posterior distribution. The section How many iterations after convergence?
provides tips on deciding how many more updates you should run. For the seeds example, try running a
further 10000 updates.
* Once you have run enough updates to obtain an appropriate sample from the posterior distribution, you
may summarise these samples numerically and graphically (see section
Obtaining summaries of the posterior distribution for details on how to do this). For the seeds example,
summary statistics for the monitored parameters are shown below:
node
alpha0
alpha1
alpha12
alpha2
sigma
mean
-0.5553
0.08693
-0.8358
1.359
0.2855
sd
0.1904
0.3123
0.4388
0.2744
0.146
MC error
0.003374
0.006873
0.01105
0.005812
0.005461
2.5%
-0.9365
-0.5502
-1.731
0.8204
0.04489
median
-0.5563
0.09157
-0.8253
1.354
0.2752
97.5%
-0.1763
0.6964
-0.002591
1.923
0.6132
start
2001
2001
2001
2001
2001
sample
20000
20000
20000
20000
20000
* To save any files created during your WinBUGS run, focus the window containing the information you
option from the
menu.
want to save, and select the
* To quit WinBUGS, select the
option from the
menu.
Monitoring parameter values
[ top | home ]
In order to check convergence and obtain posterior summaries of the model parameters, you first need to set
monitors for each parameter of interest. This tells WinBUGS to store the values sampled for those parameters;
otherwise, WinBUGS automatically discards the simulated values.
There are two types of monitor in WinBUGS:
Samples monitors: Setting a samples monitor tells WinBUGS to store every value it simulates for that
parameter. You will need to set a samples monitor if you want to view trace plots of the samples to check
convergence (see Checking convergence) or if you want to obtain 'exact' posterior quantiles, for example, the
posterior 95% Bayesian credible interval for that parameter. (Note that you can also obtain approximate 2.5%,
50% and 97.5% quantiles of the posterior distribution for each parameter using summary monitors.)
To set a samples monitor:
* Select
from the
menu;
* Type the name of the parameter to be monitored in the white box marked
;
* Click once with the LMB on the button marked
* Repeat for each parameter to be monitored.
;
Summary monitors: Setting a summary monitor tells WinBUGS to store the running mean and standard
deviation for the parameter, plus approximate running quantiles (2.5%, 50% and 97.5%). The values saved
contain less information than saving each individual sample in the simulation, but require much less storage.
This is an important consideration when running long simulations (e.g. 1000's of iterations) and storing values
for many variables.
We recommend setting summary monitors on long vectors of parameters such as random effects in order to
store posterior summaries, and then also setting full samples monitors on a small subset of the random
effects, plus other relevant parameters (e.g. means and variances), to check convergence.
To set a summary monitor:
* Select
from the
menu;
* Type the name of the parameter to be monitored in the white box marked
* Click once with the LMB on the button marked
;
* Repeat for each parameter to be monitored.
;
Note: you should not set a summary monitor until you are happy that convergence has been reached
(see Checking convergence), since it is not possible to discard any of the pre-convergence ('burn-in')
values from the summary once it is set, other than to clear the monitor and re-set it.
Checking convergence
[ top | home ]
Checking convergence requires considerable care. It is very difficult to say conclusively that a chain
(simulation) has converged, only to diagnose when it definitely hasn't!
The following are practical guidelines for assessing convergence:
* For models with many parameters, it is impractical to check convergence for every parameter, so just
choose a random selection of relevant parameters to monitor. For example, rather than checking
convergence for every element of a vector of random effects, just choose a random subset (say, the first 5
or 10).
* Examine trace plots of the sample values versus iteration to look for evidence of when the simulation
appears to have stabilised:
To obtain 'live' trace plots for a parameter:
- Select
from the
menu.
- Type the name of the parameter in the white box marked
.
- Click once with the LMB on the button marked
: an empty graphics window will appear on
screen.
- Repeat for each parameter of interest.
- Once you start running the simulations (using the
tool from the
menu), trace plots
for these parameters will appear 'live' in the graphics windows.
To obtain a trace plot showing the full history of the samples for any parameter for which you have
previously set a samples monitor and carried out some updates:
- Select
from the
menu.
(or select the name from the pull
- Type the name of the parameter in the white box marked
down list of currently monitored nodes − click once with the LMB on the downward-facing arrowhead
to the immediate right of the
field).
- Click once with the LMB on the button marked
will appear.
- Repeat for each parameter of interest.
: a graphics window showing the sample trace
The following plots are examples of: (i) chains for which convergence (in the pragmatic sense) looks
reasonable (left-hand-side); and (ii) chains which have clearly not reached convergence (right-hand-side).
If you are running more than one chain simultaneously, the trace and history plots will show each chain in
a different colour. In this case, we can be reasonably confident that convergence has been achieved if all
the chains appear to be overlapping one another.
The following plots are examples of: (i) multiple chains for which convergence looks reasonable (top); and
(ii) multiple chains which have clearly not reached convergence (bottom).
alpha0 chains 1:2
0.5
0.0
-0.5
-1.0
-1.5
101
200
400
iteration
600
alpha0 chains 1:2
10.0
7.5
5.0
2.5
0.0
-2.5
101
200
400
600
iteration
For a more formal approach to convergence diagnosis the software also provides an implementation (see here)
of the techniques described in Brooks & Gelman (1998), and a facility for outputting monitored samples in a
format that is compatible with the CODA software − see here.
How many iterations after convergence?
[ top | home ]
Once you are happy that convergence has been achieved, you will need to run the simulation for a further
number of iterations to obtain samples that can be used for posterior inference. The more samples you save,
the more accurate will be your posterior estimates.
One way to assess the accuracy of the posterior estimates is by calculating the Monte Carlo error for each
parameter. This is an estimate of the difference between the mean of the sampled values (which we are using
as our estimate of the posterior mean for each parameter) and the true posterior mean.
As a rule of thumb, the simulation should be run until the Monte Carlo error for each parameter of interest is
less than about 5% of the sample standard deviation. The Monte Carlo error (MC error) and sample standard
deviation (SD) are reported in the summary statistics table (see section
Obtaining summaries of the posterior distribution).
Obtaining summaries of the posterior distribution
[ top | home ]
The posterior samples may be summarised either graphically, e.g. by kernel density plots, or numerically, by
calculating summary statistics such as the mean, variance and quantiles of the sample.
To obtain summaries of the monitored samples, to be used for posterior inference:
* Select
from the
menu.
* Type the name of the parameter in the white box marked
(or select the name from the pull down list,
or type "*" (star) to select all monitored parameters).
* Type the iteration number that you want to start your summary from in the white box marked
: this
allows the pre-convergence 'burn-in' samples to be discarded.
* Click once with the LMB on the button marked
: a table reporting various summary statistics based
on the sampled values of the selected parameter will appear.
: a window showing kernel density plots based on
* Click once with the LMB on the button marked
the sampled values of the selected parameter will appear.
Please see the manual entry Samples... for a detailed description of the
that which appears when
is selected from the
menu).
dialog box (i.e.
Changing MCMC Defaults (advanced users only)
Contents
Defaults for numbers of iterations
Defaults for sampling methods
This section shows how to change some of the defaults for the MCMC algorithms used in WinBUGS. Users do
so at their own risk, and should make a back-up copy of the relevant default file first (although in case of
disaster a new copy of WinBUGS can always be downloaded). The program should be restarted after any edits.
Defaults for numbers of iterations
[ top | home ]
Options for iteration numbers can be changed temporarily using the Options menu (see here for details), but
the default values are held in the WinBUGS file System/Rsrc/Registry.odc which may be edited.
Examples include:
- UpdaterMetnormal_AdaptivePhase (default 4000) is the length of the adaptive phase of the general normalproposal Metropolis algorithm;
- UpdaterSlice_AdaptivePhase (default 500) is the adaptive phase of the slice sampling algorithm;
- UpdaterSlice_Iterations (default 100000) is how many tries the slice sampling algorithm has before it gives
up and produces an error message.
Defaults for sampling methods
[ top | home ]
It is now possible to change the sampling methods for certain classes of distribution, although this is delicate
and should be done carefully. The sampling methods are held in Updater/Rsrc/Methods.odc and can be edited.
For example, if there are problems with WinBUGS' adaptive rejection sampler (DFreeARS), then the method
"UpdaterDFreeARS" for "log concave" could be replaced by "UpdaterSlice" (normally used for "real non linear")
− this has been known to sort out some Traps. However, take care and don't forget to keep a copy of the
original Methods.odc file!
Distributions
Contents
Discrete Univariate
Ł
Bernoulli
Binomial
Categorical
Negative Binomial
Poisson
Continuous Univariate
Ł
Beta
Chi-squared
Double Exponential
Exponential
Gamma
Generalized Gamma
Log-normal
Logisitic
Normal
Pareto
Student-t
Uniform
Weibull
Discrete Multivariate
Ł
Multinomial
Continuous Multivariate
Ł
Dirichlet
Multivariate Normal
Multivariate Student-t
Wishart
Discrete Univariate
[ top | home ]
Bernoulli
r ~ dbern(p)
Binomial
r ~ dbin(p, n)
Categorical
r ~ dcat(p[])
Negative Binomial
x ~ dnegbin(p, r)
Poisson
r ~ dpois(lambda)
Continuous Univariate
Beta
p ~ dbeta(a, b)
Chi-squared
[ top | home ]
x ~ dchisqr(k)
Double Exponential
x ~ ddexp(mu, tau)
Exponential
x ~ dexp(lambda)
Gamma
x ~ dgamma(r, mu)
Generalized Gamma
x ~ gen.gamma(r, mu, beta)
Log-normal
x ~ dlnorm(mu, tau)
Logistic
x ~ dlogis(mu, tau)
Normal
x ~ dnorm(mu, tau)
Pareto
x ~ dpar(alpha, c)
Student-t
x ~ dt(mu, tau, k)
Uniform
x ~ dunif(a, b)
Weibull
x ~ dweib(v, lambda)
Discrete Multivariate
[ top | home ]
Multinomial
x[] ~ dmulti(p[], N)
Continuous Multivariate
[ top | home ]
Dirichlet
p[] ~ ddirch(alpha[])
Multivariate Normal
x[] ~ dmnorm(mu[], T[,])
Multivariate Student-t
x[] ~ dmt(mu[], T[,], k)
Wishart
x[,] ~ dwish(R[,], k)
References
Best N G, Cowles M K and Vines S K (1997) CODA: Convergence diagnosis and output analysis software for
Gibbs sampling output, Version 0.4. MRC Biostatistics Unit, Cambridge:
http://www.mrc-bsu.cam.ac.uk/bugs/classic/coda04/readme.shtml
Breslow N E and Clayton D G (1993) Approximate inference in generalized linear mixed models. Journal of the
American Statistical Association. 88, 9-25.
Brooks S P (1998) Markov chain Monte Carlo method and its application. The Statistician. 47, 69-100.
Brooks S P and Gelman A (1998) Alternative methods for monitoring convergence of iterative simulations.
Journal of Computational and Graphical Statistics. 7, 434-455.
Carlin B P and Louis T A (1996) Bayes and Empirical Bayes Methods for Data Analysis. Chapman and Hall,
London, UK.
Congdon P (2001) Bayesian Statistical Modelling. John Wiley & Sons, Chichester, UK.
Crowder M J (1978) Beta-binomial Anova for proportions. Applied Statistics. 27, 34-37.
Gamerman D (1997) Sampling from the posterior distribution in generalized linear mixed models. Statistics and
Computing. 7, 57-68.
Gelman A, Carlin J C, Stern H and Rubin D B (1995) Bayesian Data Analysis. Chapman and Hall, New York.
Gilks W (1992) Derivative-free adaptive rejection sampling for Gibbs sampling. In Bayesian Statistics 4, (J M
Bernardo, J O Berger, A P Dawid, and A F M Smith, eds), Oxford University Press, UK, pp. 641-665.
Gilks W R, Richardson S and Spiegelhalter D J (Eds.) (1996) Markov chain Monte Carlo in Practice. Chapman
and Hall, London, UK.
Neal R (1997) Markov chain Monte Carlo methods based on 'slicing' the density function. Technical Report
9722, Department of Statistics, University of Toronto, Canada:
http://www.cs.utoronto.ca/~radford/publications.html
Neal R (1998) Suppressing random walks in Markov chain Monte Carlo using ordered over-relaxation. In
Learning in Graphical Models, (M I Jordan, ed). Kluwer Academic Publishers, Dordrecht, pp. 205-230.
http://www.cs.utoronto.ca/~radford/publications.html
Roberts G O (1996). Markov chain concepts related to sampling algorithms. In W R Gilks, S Richardson and D
J Spiegelhalter (Eds.) Markov chain Monte Carlo in Practice. Chapman and Hall, London, UK.
Spiegelhalter D J, Best N G, Carlin B P and van der Linde A (2002) Bayesian measures of model complexity
and fit (with discussion). J. Roy. Statist. Soc. B. 64, 583-640.
Tierney L (1983) A space-efficient recursive procedure for estimating a quantile of an unknown distribution.
SIAM J. Sci. Stat. Comput. 4, 706-711.
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