cumbersome opus. However, all chapters start with a short introduction summarizing
its subject matter, to ensure that readers are aware of the scope of the chapter and can
appreciate the point of view from which the methods are addressed. Depending on
the amount of documentation already existing in statistical textbooks, some introductions are longer than others.
Overall, the book guides readers through an applied exploration of the major
methods of multivariate data analysis, as seen through the eye of an ecologist.
Starting with some exploratory approaches (Chap. 2), it proceeds logically with
the construction of the key building blocks of most techniques, i.e. association
measures and matrices (Chap. 3), and then submits example data to three families
of approaches: clustering (Chap. 4), ordination and canonical ordination (Chaps. 5
and 6), spatial analysis (Chap. 7), and finally community diversity (Chap. 8). The
methods’ aims thus range from descriptive to explanatory and to predictive and
encompass a wide variety of approaches that should provide readers with an
extensive toolbox that can address a wide palette of questions arising in contemporary multivariate ecological analysis.
1.4 How to Use This Book
The book is meant as a companion when working at the computer. The authors
pictured a reader studying a chapter by reading the text and simultaneously executing the code. To fully understand the various methods, it is preferable to go through
the chapters sequentially, since each builds upon the previous ones. At the beginning
of each chapter, an empty R console is assumed to be open. All the necessary data
files, the scripts used in the chapters, as well as the R functions and packages that are
not available through the CRAN web site, can be downloaded from our web page
(http://adn.biol.umontreal.ca/~numericalecology/numecolR/). Some of the homemade functions duplicate existing ones, providing alternative solutions (for instance
different or expanded graphical outputs), while others have been written to streamline complex sequences of operations.
Although the code provided can be run in one single copy-and-paste shot within
each chapter (with some rare exceptions for interactive functions), the best procedure
is to proceed through the code slowly and explore each set of commands carefully.
Although the use and meaning of some arguments is explained within the code or in
the text, readers are warmly invited to use and abuse of the R documentation files
(function name following a question mark) to learn about and explore the various
options available. Our aim is not to describe all options of all functions, which would
be an impossible and useless task. We are confident that an avid user, willing to go
beyond the provided examples, will be kept busy for months exploring the options
that he or she deems the most interesting.
Within each chapter, after the introduction, readers are invited to import the data
as well as the R packages necessary for the exercises of the whole chapter. The
R code used in each chapter is self-contained, i.e., it can usually be run in one step
1.4 How to Use This Book
3
its subject matter, to ensure that readers are aware of the scope of the chapter and can
appreciate the point of view from which the methods are addressed. Depending on
the amount of documentation already existing in statistical textbooks, some introductions are longer than others.
Overall, the book guides readers through an applied exploration of the major
methods of multivariate data analysis, as seen through the eye of an ecologist.
Starting with some exploratory approaches (Chap. 2), it proceeds logically with
the construction of the key building blocks of most techniques, i.e. association
measures and matrices (Chap. 3), and then submits example data to three families
of approaches: clustering (Chap. 4), ordination and canonical ordination (Chaps. 5
and 6), spatial analysis (Chap. 7), and finally community diversity (Chap. 8). The
methods’ aims thus range from descriptive to explanatory and to predictive and
encompass a wide variety of approaches that should provide readers with an
extensive toolbox that can address a wide palette of questions arising in contemporary multivariate ecological analysis.
1.4 How to Use This Book
The book is meant as a companion when working at the computer. The authors
pictured a reader studying a chapter by reading the text and simultaneously executing the code. To fully understand the various methods, it is preferable to go through
the chapters sequentially, since each builds upon the previous ones. At the beginning
of each chapter, an empty R console is assumed to be open. All the necessary data
files, the scripts used in the chapters, as well as the R functions and packages that are
not available through the CRAN web site, can be downloaded from our web page
(http://adn.biol.umontreal.ca/~numericalecology/numecolR/). Some of the homemade functions duplicate existing ones, providing alternative solutions (for instance
different or expanded graphical outputs), while others have been written to streamline complex sequences of operations.
Although the code provided can be run in one single copy-and-paste shot within
each chapter (with some rare exceptions for interactive functions), the best procedure
is to proceed through the code slowly and explore each set of commands carefully.
Although the use and meaning of some arguments is explained within the code or in
the text, readers are warmly invited to use and abuse of the R documentation files
(function name following a question mark) to learn about and explore the various
options available. Our aim is not to describe all options of all functions, which would
be an impossible and useless task. We are confident that an avid user, willing to go
beyond the provided examples, will be kept busy for months exploring the options
that he or she deems the most interesting.
Within each chapter, after the introduction, readers are invited to import the data
as well as the R packages necessary for the exercises of the whole chapter. The
R code used in each chapter is self-contained, i.e., it can usually be run in one step
1.4 How to Use This Book
3