viii Preface
move on to looking at the results for individual variables. And MANOVA for
repeated measures has been largely superseded by the models that we shall
describe in Chapter 8. Second, a classification technique such as LDF needs
to be considered in the context of modern classification algorithms, and these
cannot be covered in an introductory book such as this.
Some brief details of the theory behind each technique described are given,
but the main concern of each chapter is the correct application of the meth-
ods so as to extract as much information as possible from the data at hand,
particularly as some type of graphical representation, via the R software.
The book is aimed at students in applied statistics courses, both under-
graduate and post-graduate, who have attended a good introductory course
in statistics that covered hypothesis testing, confidence intervals, simple re-
gression and correlation, analysis of variance, and basic maximum likelihood
estimation. We also assume that readers will know some simple matrix alge-
bra, including the manipulation of matrices and vectors and the concepts of
the inverse and rank of a matrix. In addition, we assume that readers will
have some familiarity with R at the level of, say, Dalgaard (2002). In addition
to such a student readership, we hope that many applied statisticians dealing
with multivariate data will find something of interest in the eight chapters of
our book.
Throughout the book, we give many examples of R code used to apply the
multivariate techniques to multivariate data. Samples of code that could be
entered interactively at the R command line are formatted as follows:
R> library("MVA")
Here, R> denotes the prompt sign from the R command line, and the user
enters everything else. The symbol + indicates additional lines, which are
appropriately indented. Finally, output produced by function calls is shown
below the associated code:
R> rnorm(10)
[1] 1.8808 0.2572 -0.3412 0.4081 0.4344 0.7003 1.8944
[8] -0.2993 -0.7355 0.8960
In this book, we use several R packages to access different example data sets
(many of them contained in the package HSAUR2), standard functions for the
general parametric analyses, and the MVA package to perform analyses. All of
the packages used in this book are available at the Comprehensive R Archive
Network (CRAN), which can be accessed from http://CRAN.R-project.org.
The source code for the analyses presented in this book is available from
the MVA package. A demo containing the R code to reproduce the individual
results is available for each chapter by invoking
R> library("MVA")
R> demo("Ch-MVA") ### Introduction to Multivariate Analysis
R> demo("Ch-Viz") ### Visualization
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