Nonlinear Regression with R (Use R) Review

Average Reviews:

(More customer reviews)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.

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- Coherent and unified treatment of nonlinear regression with R.- Example-based approach.- Wide area of application.

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