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repeated measures has been largely superseded by the models that we shall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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