Statistical Computing with R (Chapman & Hall/CRC The R Series) [ハードカバー]

This book is another text that covers statistical methods using R. Many of the others are specialized as for example Mark Chang's which covers adaptive designs only and Jim Albert's deals only with Bayesian methods. This is the best that I have seen to cover advanced statistical topics and teach the essentials of R for statistical analysis. So someone like me, who is not very familiar with R, can learn quickly how to apply it. The basics are covered in Chapter 1 and the author explains where to find the good R libraries and how to download the software which is free.

Chapter 2 is a short review of probability and statistics. This is provided to help the reader understand the material on generating Monte Carlo data for univariate and multivariate probability distributions that comes in Chapter 3.

The other chapters cover visualization of multivariate data, Monte Carlo methods, integration and variance reduction techniques and advanced topics including the bootstrap and the jackknife, permutation test, MCMC methods and working with data arrays and frames. All these methods are illustrated with R code.

For the most part the author provides excellent references to the literature and texts. The coverage of bootstrap is exceptional. My book, Efron and Tibshirani, Davison and Hinkley and Shao and Tu are all referenced. There is a thorough treatment of bootstrap confidence intervals covering all the high order methods and their properties. There is even a discussion about the jackknife estimate of the acceleration constant (illustrated with R code) and mention of an alternative method available in the book by Shao and Tu. The text includes 299 references.

The only glaring omission I found was in the discussion of permutation tests. There have been major books written by Good, Edgington. Manly and Pesarin that were overlooked.

Practitioners in statistics need two things: Understanding of the subject and a tool to help them explore data. R is clearly the tool of choice, providing a range of capabilities. There are several introductory books on R, but none also provide the grounding in statistics that Rizzo's does.

To a new student of the subject, I'd recommend this, and Gelman and Hill's DATA ANALYSIS USING REGRESSION AND MULTILEVEL/HIERARCHICAL MODELS.

Good overall, would like more ecological examples provided. Solid introduction, good algebraic knowledge required. Six more words required, six more words required.