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Overheads -- Multilevel models
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Overheads for a talk on multilevel models. Note that later slides canvass
fairly technical issues.
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Chapter 10, using lme()
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R code & output, with brief commentary, for using lme() (package nlme) in
place of lmer() (lme4)
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Computations - Linear and GAM models
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These describe the computational methods used by lm() and by the gam()
function in the mgcv package. They show how to make direct
use of R's suite of functions (qr() and friends) for working directly
with the QR matrix decomposition. They give technical detail that
underpin the calculations of chapters 6-9. [Lund]
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Generalized Linear Models
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Brief notes on the theory of generalized linear models, and on the comparison
with linear models. [Lund]
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Regression in practice
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Issues for the practical use of regression methods, supplementing the
discussion in the text. [Lund]
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Smoothing terms in GAM models
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Automated choice of smoothing parameter for smoothing terms in models
with independent normal errors, in logistic regression models, and in
Poisson regression models. This expands the very brief discussion
at the end of Chapter 7.
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Classification
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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(). Additionally, there are comparisons
with randomForest(), from the randomForest package. In a
range of methodologies from parametric to
nonparametric, lda() and qda() are at the
extreme parametric end, while randomForest() is about as
non-parametric as is possible.
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Ordination -- Low-Dimensional Representation
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These notes describe distance measures, representation in a Euclidean
space (metric scaling), and multi-dimenensional scaling.
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Analysis of microarray data
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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 4.4.1.
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Spatial methods in R
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This has notes and slides that may be helpful in making a start on the
use of R packages for spatial methods -- a topic that is not covered
in the book. Code is provided for the graphs that are included in the
overheads.
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R talks to LaTeX
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Use knitr
or Sweave
to process a document that includes R code within Sweave type markup,
to generate a LaTeX document that may include any or all of R code,
output, tables and graphs. The more flexible knitr markup may now
be preferred. For knitr, production of a Markdown html
document is an alternative.
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