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This book is about nonlinear regression analysis with R, in particular, how
to use the function nls() and related functions and methods.
Range of the book
Nonlinear regression may be a confined and narrow topic within statistics.
However, the use of nonlinear regression is seen in many applied sciences,
ranging from biology to engineering to medicine and pharmacology. Therefore,
this book covers a wide range of areas in the examples used. Appendix A lists
the disciplines from which data are used in this book.
What not to expect
This book is not a textbook on nonlinear regression. Basic concepts will be
briefly introduced, but the reader in need of more explanations will have to
consult comprehensive books on nonlinear regression such as Bates and Watts
(1988) or Seber and Wild (1989). Instead, this book may be particularly wellsuited
as an accompanying text, explaining in detail how to carry out nonlinear
regression with R. However, we also believe that the book is useful as a standalone,
self-study text for the experimenter or researcher who already has some
experience with R and at the same time is familiar with linear regression and
related basic statistical concepts.
Prerequisites
Experience with R at a level corresponding to the first few chapters in Dalgaard
(2002) should be sufficient: The user should be acquainted with the
basic objects in R such as vectors, data frames, and lists, as well as basic
plotting and statistics functions for making scatter plots, calculating descriptive
statistics, and doing linear regression. |
