Nonlinear Regression with R

  • Christian Ritz
  • Jens Carl Streibig

Part of the Use R book series (USE R)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Pages 1-3
  3. Pages 7-21
  4. Pages 37-54
  5. Pages 55-71
  6. Pages 109-131
  7. Back Matter
    Pages 133-145

About this book

Introduction

R is a rapidly evolving lingua franca of graphical display and statistical analysis of
experiments from the applied sciences. Currently, R offers a wide range of functionality for nonlinear regression analysis, but the relevant functions, packages and documentation are scattered across the R environment. This book provides a coherent and unified treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
The book begins with an introduction on how to fit nonlinear regression models in R. Subsequent chapters explain in more depth the salient features of the fitting function nls(), the use of model diagnostics, the remedies for various model departures, and how to do hypothesis testing. In the final chapter grouped-data structures, including an example of a nonlinear mixed-effects regression model, are considered.
Christian Ritz has a PhD in biostatistics from the Royal Veterinary and Agricultural University. For the last 5 years he has been working extensively with various applications of nonlinear regression in the life sciences and related disciplines, authoring several R packages and papers on this topic. He is currently doing postdoctoral research at the University of Copenhagen.
Jens C. Streibig is a professor in Weed Science at the University of Copenhagen. He has for more than 25 years worked on selectivity of herbicides and more recently on the ecotoxicology of pesticides and has extensive experience in applying nonlinear regression models. Together with the first author he has developed short courses on the subject of this book for students in the life sciences.

Keywords

Fitting Regression analysis grouped data linear regression regression diagnostics self starter functions transform-both-sides approach

Editors and affiliations

  • Christian Ritz
    • 1
  • Jens Carl Streibig
    • 2
  1. 1.Department of Basic Sciences and Environment (Statistics)Faculty of Life Sciences University of CopenhagenFrederiksberg CDenmark
  2. 2.Department of Agriculture and Ecology (Crop Science)Faculty of Life Sciences University of CopenhageTaastrupDenmark

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-09616-2
  • Copyright Information Springer New York 2008
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-09615-5
  • Online ISBN 978-0-387-09616-2
  • About this book