ACSPRI Course: Modern Regression, Classification and
Multivariate Exploration  January 1620 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 R2.14.0 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:
http://www.maths.anu.edu.au/~johnm/courses/r/notes/rnotes140.pdf
Participants whose main exposure to R is from immediate precourse
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!
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.
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.
http://www.maths.anu.edu.au/~johnm/dmwkshp.html
JM's Data Mining Links
Go to the web page
Data mining links
Links

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

John Maindonald's web site

email: john.maindonald AT anu.edu.au
Last updated: November 3 2011.