The majority of data sets collected by researchers in all disciplines are multivariante. In a few cases it may be sensible to isolate each variable and study it separately, but in most case all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be most helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in a general sense, to display or extract the signal in the data in the presence of noise, and to find out what the data show us in the midst of their apparent chaos . The computations involved in applying most multivariate techniques are considerable, and their routine use requires a suitable software package. In addition, most analyses of multivariate data should involve the construction of approapriate graphs and diagrams and this will also need to be carried out by the sample packe. R and S-PLUS® are statistical computing environments, incorporating implementations of the S programming language. Both are powerful, flexible, and, in addition, have excellent graphical facilities. It is for these reasons that they appear in this book. In this book we concentrate on what might be termed the "core" multivariate methodology, although mention will be made of recent developments where these are considered relevant and useful. Some basic theory is given for each technique described but not the complete theoretical details; this theory is separated out into "displays". Suitable R and S-PLUS code (which is often identical) is given for each application. All data sets and code used in the book can be found at http://biostatistics.iop.kcl.ac.uk/publications/everitt/. In addition, this site contains the code for a number of functions written by the author and used at a number of places in the book.
