| Nonlinear Regression with R 2008 Edition Contributor(s): Ritz, Christian (Author), Streibig, Jens Carl (Author) |
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ISBN: 0387096159 ISBN-13: 9780387096155 Publisher: Springer OUR PRICE: $85.49 Product Type: Paperback - Other Formats Published: November 2008 Annotation: 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 starts out giving a basic introduction to fitting nonlinear regression models in 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. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - General - Medical | Pharmacology - Medical | Epidemiology |
| Dewey: 519.5 |
| LCCN: 2008938643 |
| Series: Use R! |
| Physical Information: 0.5" H x 6.1" W x 9.1" (0.50 lbs) 148 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |