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Titlebook: Nonlinear Regression with R; Christian Ritz,Jens Carl Streibig Book 2008 Springer-Verlag New York 2008 Fitting.Regression analysis.grouped

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书目名称Nonlinear Regression with R
编辑Christian Ritz,Jens Carl Streibig
视频video
概述Is unique because it approaches non-linear regression modeling through the functionality available in R,.such that the analysis in many respects is carried out using the same commands and functions as
丛书名称Use R!
图书封面Titlebook: Nonlinear Regression with R;  Christian Ritz,Jens Carl Streibig Book 2008 Springer-Verlag New York 2008 Fitting.Regression analysis.grouped
描述.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..
出版日期Book 2008
关键词Fitting; Regression analysis; grouped data; linear regression; regression diagnostics; self starter funct
版次1
doihttps://doi.org/10.1007/978-0-387-09616-2
isbn_softcover978-0-387-09615-5
isbn_ebook978-0-387-09616-2Series ISSN 2197-5736 Series E-ISSN 2197-5744
issn_series 2197-5736
copyrightSpringer-Verlag New York 2008
The information of publication is updating

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Uncertainty, Hypothesis Testing, and Model Selection,ar approximation may or may not be used in the estimation algorithm (e.g., the Gauss-Newton algorithm), but more importantly it is often used to obtain standard errors, confidence intervals, and t-tests, all of which will depend on how good the approximation is..The quality of the linear approximati
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