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
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March 2, 2007. This website has been included in Intute `Intute is a free online service providing you with access to the very best Web resources for education and research, selected and evaluated by a network of subject specialists. The service is brought to you by a consortium of UK universities and partners.'

Until I retired at the end of September, 2005, I was the Director of Studies for the MPhil in Statistical Science. The new Director of Studies is Dr S.M.Pitts.
My current interests are principally statistical consulting and the use of R/S-Plus.
Due to severe osteo-arthritis in both knees, I had a total knee replacement of the left knee in October 2008, and expect to have the same operation on the right knee in due course.

  • 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.

    I set down my memories of my computing education in the seminar I was invited to give at the University of Piraeus in May 2006.

    Recently (ie 2007-8) 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)

  • Introduction to S-PLUS for generalized linear modelling: postscript
  • Introduction to S-PLUS for generalized linear modelling: pdf
  • The general linear model: what you need to know
  • Solutions to S-PLUS glm worksheets: (postscript)
  • Solutions to S-PLUS glm worksheets:(pdf)
  • Other worksheets for R (or S-PLUS): multivariate analysis etc (postscript)
  • Other worksheets for R (or S-PLUS): multivariate analysis etc (pdf)
  • Other worksheets for R (or S-PLUS): multivariate analysis etc (gzipped postscript)
  • Lecture Notes and exercises for Applied Multivariate Analysis (postscript)
  • Lecture Notes and exercises for Applied Multivariate Analysis (pdf)
  • Using S-PLUS for teaching (March 1998)


    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.)


    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).
  • `Spoetry'

  • `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
  • Prof Joseph Ferrie
    My publications and other notes Seminar slides Personal