ACSPRI Course: Modern Regression, Classification and Multivariate Exploration -- January 16-20 2012

In Preparation for the Course

Participants will be expected to bring their own laptops (PC or MacOS X or Linux), with a recent version of R (preferably R-2.14.0 or later) already installed.

The web page Course preparation has details of packages that should be installed. Included also is a link to instructions that may be helpful in setting up R under Windows, and links to further documents. Please contact the tutor in case of difficulty.

Intending participants with limited previous experience of the R system should as a minimum do the exercises noted under Course preparation, and work through chapters 1 and 2 (at least) of the tutorial notes that are available from:
Participants whose main exposure to R is from immediate pre-course preparation should expect a steepish learning curve as the course progresses -- not a problem for those who take the learning of computer languages in their stride!

Draft course schedule

Modern Regression in a Wider Statistical Context.

This is the first chapter of the course notes. These notes set the content of this course in a wider statistical context, but staying as far as possible at the level of elementary statistical ideas. Some preliminary familiarity with this material will be very helpful. For running the computations, it will be necessary to install the modregR package: Download zip file from this directory. [NB: This is not (yet) available from CRAN.] After downloading it, check the README.txt file in the above directory for instructions.

Datasets, and familiarisation exercises.

Familiarity with these datasets will help in following the tutorials and doing course exercises. R code is given that can be used to get summary information and to plot graphs that will help reveal important features of the data.

Slides that Give an Overview of R

These give an overview of the R language Later slides describe some of R's statistical analysis abilities.

Statistical Learning and Data Mining - Introductory Notes and Slides

These introduce ideas and themes that will feature in the course, but from a perspective that connects more closely with the data mining literature.

JM's Data Mining Links

Go to the web page Data mining links


Web site for R (CRAN = Comprehensive R Archive Network)

John Maindonald's web site

email: john.maindonald AT
Last updated: November 3 2011.