I'm writing this somewhat cranky review as I read the book. Compared to the Pinheiro and Bates book, the examples in this book are initially irritatingly difficult to get working. A major problem with the book is that code involving BUGS runs only on Windows. This excludes readers like me from the action. So I have to wait until I get a Windows machine--but do I really want to start using Windows now? It would have been more helpful if their webpage prominently mentioned this detail (that the book is Windows specific). Had they done that I would probably not have bought it. But now that I have paid for it I am going to read it.
The website for the book has the data in a pretty disorganized way--why not just make a library? The authors do have a package for arm on the CRAN archive, but it does not install on any OS except Windows (the first R package I have seen with this property in my seven years as an R user). I tried to wget -r the ~gelman/arm/examples directory but ended up with all kinds of other crap in my directory as well, which was annoying. A zip archive could not hurt.
Chapters 1-3
I did not get a huge amount out of these chapters that was deeply interesting, but it is a good intro for newcomers to regression.
The code for the example in chapter 3 doesn't work on non-windows machines. Here is a working version.
Chapter 4
The book becomes more and more exciting from about this point onwards. Only one grouse:
Chapter 4 has some principles doing carrying out regression for prediction (section 4.6) but it is far from clear where they come from and the principles have a cookbookey feel (do this, don't do that, without explaining why). It would have been better if the authors had taught the reader to reason about the problem (surely those are the real principles, and the presented principles the consequences of the thought process generated by those principles).
[to be continued]
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