Nonlinear Regression with R

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.

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. R. Subsequent chapters explain the salient features of the main fitting function nls (), the use of model diagnostics, how to deal with various model departures, and carry out hypothesis testing. In the final chapter grouped-data structures, including an example of a nonlinear mixed-effects regression model, are considered.