Data Analysis and Graphics Using R - An Example-Based Approach

John Maindonald and John Braun     2nd edn, Cambridge University Press, January 2007    

Additional Notes

Note that the notes on linear computations, on generalized linear models and on classification, are technically demanding.
Updates I (7Dec2007) Supplementary notes re xyplot() (pp.55-56, etc), AOV table on p.121, measurement error models (pp.207, 209), predict() for GLMs (Ch 8), etc.
Updates II (23Dec2008) GUIs, easier ways in lattice, ARIMA modeling using the forecast package, changes to mcmcsamp() output objects.
Overheads -- Talk on multilevel models Overheads for a talk on multilevel models. Note that later slides canvass very technical issues.
Chapter 10, using lme() R code & output, with brief commentary, for using lme() (package nlme) in place of lmer() (lme4)
Lattice & other graphics (4Apr2009) These notes substantially supplement the discussions of lattice in Sections 1.6.8 and 14.12, and the lattice code in the text. They discuss some newer lattice features that are not covered in the book. The playwith package warrants more than the current very brief mention. The latticist() function, called with a data frame as argument, starts a GUI interface to lattice. The playwith() function allows interactive enhancements to lattice and other R plots.
Least squares computation Computational methods used by lm(), with notes on the direct use of R's suite of functions (qr() and friends) for working directly with the QR matrix decomposition. These are peripherally relevant to chapters 6-9. [Lund]
Generalized Linear Models Brief notes on the theory of generalized linear models, and on the comparison with linear models. [Lund]
Regression in practice Issues for the practical use of regression methods, supplementing the discussion in the text. [Lund]
Smoothing terms in GAM models Automated choice of smoothing parameter for smoothing terms in models with independent normal errors, in logistic regression models, and in Poisson regression models. This leads on from the discussion of Chapter 7.
Classification Notes on the theory that underpins the functions lda() and qda() in R's MASS package. For the case of two outcome classes, comparisons are made with logistic regression using glm().
Analysis of microarray data The package DAAGbio has a vignette, and associated files and datasets, that demonstrates the analysis of the two-channel microarray data that are described in Section 4.2.2.
R talks to LaTeX Process a document that includes R code within Sweave markup, to generate a LaTeX document that may include any or all of R code, output, tables and graphs.
[Lund] These notes were developed for use with a PhD course, conducted under the STINT program, in the Centre for Mathematical Statistics at the University of Lund (Sweden) in May-June 2007.