
Updates I (7Dec2007)

Supplementary notes re xyplot() (pp.5556, 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 69. [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 twochannel 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.
