Computational Statistics: An Introduction to R
Computational Statistics: An Introduction to R
复制标题
计算统计：R 简介
DOI：
--
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Dirk Eddelbuettel
中科院分区：
文献类型：
--
作者：
Dirk Eddelbuettel
《计算统计学：R导论》是一本写得很好、组织得很好的书，适合在定量和计算方面有经验的读者。正如作者所写的那样，“这篇R的介绍旨在作为简明课程或自学的课程材料。该课程是为具有随机学基础知识的学生开设的。”(p. v）本书分为四个主要章节：“基本数据分析”，“回归”，“比较”和“维度1，2，3，. . .，∞.”为了介绍R在单样本分析和分布，回归模型，双样本问题和比较分布以及多变量分析中的应用。每一章都很好地将随机过程和统计理论与示例以及相关的R代码和输出相结合。章节结束时参考文献，以便有兴趣进一步深入研究特定主题的读者可以这样做，参考文献在书的最后汇总成一个完整的列表。在我看来，正是有趣的例子和相关的R代码的集成使文本成为阅读和工作的乐趣。这些例子既不过于琐碎（尽管有些是熟悉的，比如无处不在的虹膜数据），也不过于复杂，R代码也同样可以访问，既不太简单也不太复杂。在整个过程中，每个要点和例子后面都有练习，以便读者可以练习和测试理解。本书最后有一个附录，总结了R编程语言和环境，其中有组织良好的列表，包含函数描述和其他有用的信息。一个基本的同伴网站http://sintro.r-forge.r-project.org/为那些可能在短期课程中使用这本书的人提供了一些额外的材料。此外，文本包含三个单独的索引。第一个，函数和变量主题，很好地组织R函数的主题，如“高级图”，“数据操作”等，我特别喜欢这个索引，因为新的R用户的主要学习障碍之一是需要知道函数的名称来查找其相关的帮助文件。第二个，函数和变量索引，提供了R函数和变量的字母索引。第三，主题索引，正是标题所暗示的。
A compact and efficient introduction to R, Computational Statistics: An Introduction to R is a well-written and nicely organized book suitable for quantitatively and computationally sophisticated readers. As the author writes, “This introduction to R is intended as course material to be used in a concise course or for self-instruction. The course is for students with basic knowledge in stochastics.” (p. v) The book is divided into four main chapters: “Basic Data Analysis,” “Regression,” “Comparisons,” and “Dimensions 1, 2, 3, . . . ,∞.” In order, they introduce the application of R for one-sample analysis and distributions, regression models, two-sample problems and comparing distributions, and multivariate analysis. Each chapter nicely integrates stochastic processes and statistical theory with examples and associated R code and output. The chapters conclude with references to the literature, so that readers interested in delving further into a particular topic can do so, and the references are aggregated into a complete list at the end of the book. In my opinion, it is the integration of interesting examples and associated R code that make the text a pleasure to read and work through. The examples are neither overly trivial (though some are familiar, such as the ubiquitous iris data) nor excessively complicated, and the R code is similarly accessible without being either too simple or complex. Throughout, each of the main points and examples are followed by exercises so that readers can practice and test comprehension. The book concludes with an appendix summarizing the R programming language and environment with well-organized lists containing function descriptions and other useful information. A rudimentary companion site at http://sintro.r-forge.r-project.org/ provides some additional materials for those that might use the book for a short course. In addition, the text contains three separate indices. The first, Functions and Variables by Topic, nicely organizes R functions by topics such as “High-Level Plots,” “Data Manipulation,” etc. I particularly like this index since one of the main learning impediments for new R users is the catch-22 of needing to know the name of a function to look up its associated help file. The second, Function and Variable Index, provides an alphabetical index of R functions and variables. And, the third, Subject Index, is just what the title implies.