Short Course at the Postgraduate Statistics Centre, Lancaster University
Graphical Modelling of Observational Data using R
Date: 20 - 21 July 2011
Duration: 2 Days
Presenter: Joe Whittaker
Website: http://shortcourses.maths.lancs.ac.uk/graphicalmodels
A particular problem of statistical data analysis is to model the
inter-dependencies among a set of response variables. Graphical
models, based on conditional independence, provide a powerful and
informative toolbox for unravelling manifest interactions and
associations.
This course discusses undirected graphical models, describing their
properties and their statistical analysis. The course is not
mathematical, but sometimes phrased in mathematical notation, and is
loosely based on material from Whittaker (1990) Graphical models in
applied multivariate statistics, Wiley.
Novel features of this course are the emphasis on weighted
independence graphs and a survey of R packages useful to graphical
modelling. Many examples of successful analyses drawn from the
social, biological and physical sciences are given.
The course is aimed at researchers and research students who have
experience of statistical modelling (up to linear regression) and
hypothesis testing, who wish to develop techniques to analyse
multivariate data.
The aim of the course is to provide a background of theory with
opportunities to apply the techniques in practice, and each session
consists of a lecture/ demonstration and a practical. Participants
are encouraged to bring their own data set to explore during the
course. The software package used is R and participants are expected
to have some basic familiarity.
The following topics are covered
. conditional independence, independence graphs, mutual information.
. graphical Gaussian models, maximum likelihood estimates, explained
information.
. graphical log-linear models for categorical data, MLEs,
. model search, trees, score and constrained search - in brief.
The illustrations use R-packages such as gRim, Rgraphviz, ggm, minet
and several others.
Learning
Participants learn through the application of concepts and techniques
covered in the course to real data sets, including their own. Students
are encouraged to examine issues of substantive interest in these
studies. Successful students will be:
. familiar with graphical models and conditional independence,
. able to investigate data with exploratory graphical models,
. able to confirm hypotheses encapsulated in models,
. able to apply theoretical concepts,
. able to identify and solve problems,
. able to analyse data using appropriate techniques and interpret statistical
output.
To book a place on the course, please access our short course booking
website:
http://shortcourses.maths.lancs.ac.uk/graphicalmodels
or
http://shortcourses.maths.lancs.ac.uk.
Or contact the Short Course Administrator, Angela Mercer on 01524 593064 or
email [log in to unmask]
See our e-brochure at www.maths.lancs.ac.uk/psc.
Regards
Angela
Angela Mercer
Short Course/Postgraduate Administrator
Applied Statistics
C/o Postgraduate Statistics Centre
Room 78
Lancaster University
Lancaster
***Please note I work Mon-Fri, 9.30 - 2.45 pm***
* Tel: +44 (0) 1524 593064
Fax: +44 (0) 1524 592681
Email: [log in to unmask]
http://shortcourses.maths.lancs.ac.uk
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