Home Page: P.M.E. Altham

Dr P.M.E. Altham
Statistical Laboratory
University of Cambridge
Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WB, UK
Tel: +44 1223 337954
Fax: +44 1223 337956
Email: P.M.E.Altham@statslab.cam.ac.uk

From March 2, 2007, this website has been included in Intute

Until I retired at the end of September, 2005, I was the Director of Studies for the MPhil in Statistical Science.

My current interests are principally categorical data, statistical consulting and the use of R.

March 2012: Robin Hankin and I have a new paper accepted by the Journal of Statistical Software, giving multivariate generalizations of `my' multiplicative binomial distribution, and the corresponding R package.

Some of the computer languages I have had to try to learn since graduating in 1964: Cambridge autocode, algol, phoenix, machine-code, Fortran, BBC-Basic, GLIM, GENSTAT, Linux, S-Plus and finally (probably the best so far!) R. I am lucky enough to have had many generations of excellent students to help me with this continuous learning.

Recently I have paid particular attention to revising my R computing worksheets, to include more graphs and to improve the presentation. I have also expanded my undergraduate lecture notes for the Introduction to Generalized Linear Modelling course.

Teaching: these notes etc are NOT TO BE QUOTED WITHOUT ACKNOWLEDGEMENT, please.

  • These notes all relate to courses given before September 2005. I add little things to them from time to time.

    MPhil/Part III: Applied Statistics, and Applied Multivariate Analysis (graduate courses)

  • The general linear model: what you need to know
  • Statistical modelling worksheets in R
  • Other worksheets for R (or S-PLUS): multivariate analysis etc
  • Lecture Notes and exercises for Applied Multivariate Analysis

    Useful links include, firstly your FREE access to R via R (This is a mirror site, courtesy of Bristol University.)
    For a very clear and full account of linear modelling, using R, see `Practical Regression and Anova in R' by Julian Faraway (2002)
    Excellent online introductions to R are

  • `Using R for Data Analysis and Graphics: An Introduction' by J.H.Maindonald (2004)

  • `An Introduction to R' by W.N.Venables and D.M.Smith, and the R development team (this is also available as a paperback.)

  • Kickstarting R, by Jim Lemon

    Knowledge of R is very important in computational biology and bioinformatics: see the Bioconductor project.
    Other useful links are

  • `A not so short introduction to LaTeX' by Tobias Oetiker.
  • Elie Bazouls' introduction to Latex
  • `simpleR - Using R for Introductory Statistics' by John Verzani (2002).
  • `Categorical Data Analysis' (2002) by Alan Agresti

  • `Statistical Computing: an Introduction to Data Analysis Using S-Plus' (2002) by Michael Crawley

  • Michael Friendly's wonderful graphics.

  • Professor Jim Lindsey.

  • `LTSN: Maths, Statistics & OR Network'

  • `Generalized Linear Models (2002) by German Rodriguez

  • by Vincent Zoonekynd, for French speakers

  • Peter Dunn's homepage

  • Paul Johnson's Stats`R`us (tips on R)

  • R for computational finance

  • `Data-mining with R: learning by case studies' by Luis Torgo. This includes financial applications of R, eg predicting stock market returns.

  • `Guide to the web for Statisticians' STATSCI
  • DASL (data and story library)
  • US Federal Reserve Exchange Rates
  • Dr Laura Thompson's very useful S-Plus/R companion to Alan Agresti's classic text on categorical data analysis
  • Wonderful graphics, topical world data, from gapminder
  • David M.Smith writes about R every weekday at his Revolutions blog
  • Prof Joseph Ferrie
  • Robert A.Muenchen writes about `The Popularity of Data Analysis Software'
    My publications and other notes Seminar slides Personal