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Nonlinear Regression with R (Use R!)
Ebook Free Nonlinear Regression with R (Use R!)
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Review
From the reviews:"The Use R! series published by Springer is a wonderful thing. There is nothing else like it, and as far as I know there has never been anything like it, certainly not for open source software…. This book by Ritz and Streibig is a fine example. It documents, explains, and illustrates in considerable detail the venerable nls() function, available both in S and R, … a mini-course in nonlinear regression…. Books of this form are ideal for self-study,…. My guess is that about 95% of the experimenters or researchers using this book will think that the material is quite sufficient for their needs, and will not be interested in further theoretical studying. That, I think, is actually one of its greatest strengths. … In summary I think the book is excellent, and eminently useful. I hope it will serve as a model for documenting more of the larger R functions and packages." (Jan de Leeuw, Journal of Statistical Software, 2009)."Readership: Under- and post-graduate students of statistics and of applied disciplines in biology, chemistry, engineering, fisheries science, medicine and toxicology. … The scope and topic of this book are in the title and the authors take as their starting point the function nls() and subsequently, related functions in R. … I strongly recommend this book – if you are a young scientist … then this book will save you hours of wasted exploration and investigation to find the allusive solution to your nonlinear estimation problem." (C. M. O’Brien, International Statistical Review, Vol. 77 (3), 2009)“…A brief and focused book in Springer’s ‘Use R!’ series. …The information about nonlinear regression methodology and advice on how to use it is accurate and useful; the examples are novel and effective … and the authors provide enough information for practitioners who have little experience with nonlinear regression to begin to fit simple nonlinear models and draw inferences from them. … Useful as a secondary text for an applied course on non-linear regression, providing students a tutorial on implementation in R and even some exercises that could be used in such a course or for self-study. I congratulate Ritz and Steibig on a informative and well-written little book. †(The American Statistician, February 2010, Vol. 64 No.1)“The preface of this book clearly spells out its intended purpose: it is a how-to book on the use of the nls function in R, rather than a textbook on nonlinear regression. As such, it is intended as a reference for readers with some past experience with R and a reasonable working knowledge of linear regression, or as a supplementary text for a course on nonlinear regression. It serves both purposes pretty well and I judge it to be a handy little book… .†(Biometrics, Summer 2009, 65, 1001)
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From the Back Cover
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.
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Product details
Series: Use R!
Paperback: 148 pages
Publisher: Springer; 2008 edition (November 21, 2008)
Language: English
ISBN-10: 0387096159
ISBN-13: 978-0387096155
Product Dimensions:
6.1 x 0.4 x 9.2 inches
Shipping Weight: 10.9 ounces (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
4 customer reviews
Amazon Best Sellers Rank:
#1,435,183 in Books (See Top 100 in Books)
Nonlinear Regression with R (NRwR) offers an example driven tour of R's base nonlinear regression tool, nls(). As you can glean from the table of contents, NRwR covers nonlinear models, generalized linear models, self-starting functions and model diagnostics (tools for inference as well). It does so in ~113 pages. BE WARNED. The book looks short because it is. Most of the material is helpful, but you are paying close to 50 bucks for a relatively short book.High points:-Model diagnostics are handled fairly well. The book walks you through checking for heteroskedacity, writing a function to test a hypothesis and dealing with mis-specified errors.-Examples abound. If you read this book as it was intended, you will download the nlrwr package and follow along as the authors walk through datasets. Each page has sample code allowing you to reproduce the graphs and results in the book.-Length can be an advantage. If you aren't particularly interested in the theory behind nonlinear regression, you wont be encumbered by it here.-The bibliography is broken down by theme and is genuinely helpful.Low Points:-As reliant as the book is on nls(), I would love to have seen more digging into the peculiarities of this function. Chapter 4 is devoted to nls(), but it is a mere 16 pages, one of which is basically the gist of ?nls. I know it is beyond the scope, but I would have been excited to see more detail on nls so we can use the output from a nonlinear least squares problem as a building block to a larger function.- organization for the book is a bit odd. The introduction is comprised of three examples of nonlinear regression in the field. It is about six pages and seems to have been cobbled together. The book would have been better served by a more thorough introduction.-The topics covered are relatively narrow.-Length is a serious factor.All in all this is a good effort which would be easily 4 or 5 stars at a lower price point. I'm happy I bought it, but if your budget is constrained and you are forced to choose between this and another book on R's internals, think hard about the pros and cons.
This is a neat book. It's a great way to get an introduction to nonlinear regression via the use of R. It does not require any sort of heavy-hitting theoretical background; you can find other books (e.g., Seber and Wild) to fill in the details. By carefully studying the examples and the R code, one can definitely learn a few things that will serve as grist for future reading and applications. It's small size probably means you'll actually read it and suits its intended purpose!
This book is pretty concise and to the point: It will show you how to do non-linear regression with R but will teach you neither. If you are familiar with non-linear regression methodologies and have some familiarity with R, this book will have you using R for your non-linear regression needs in a snap. In this sense the short length and limited scope are an asset.If you are not familiar with the theory behind non-linear regression I would certainly recommend complementing this book with a more in depth treatment of the subject. I found Bates and Watts to be very balanced in terms of presenting important information without being overwhelmingly mathematical. On the other hand, if you never used R, then you will need to learn it elsewhere as the book assumes certain level of familiarity with the environment.In short, the book is a glorified tutorial on nls() and got me doing NLR in R pretty quickly. In this sense I recommend it and if price is not an issue I would think it is probably a good reference to have around. However, as other reviewers pointed out, the price seems disproportionate especially considering that there are other resources on-line that will do the trick. The price would be fairer if the book included tutorials in other related areas such as Bayesian inference with non linear models or an expanded treatment of non-linear mixed effects models, just to throw in some ideas.
Very good!
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