S Programming

Author: William Venables
Publisher: Springer Science & Business Media
ISBN: 0387218564
Format: PDF, Docs
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S is a high-level language for manipulating, analysing and displaying data. It forms the basis of two highly acclaimed and widely used data analysis software systems, the commercial S-PLUS® and the Open Source R. This book provides an in-depth guide to writing software in the S language under either or both of those systems. It is intended for readers who have some acquaintance with the S language and want to know how to use it more effectively, for example to build re-usable tools for streamlining routine data analysis or to implement new statistical methods. One of the outstanding strengths of the S language is the ease with which it can be extended by users. S is a functional language, and functions written by users are first-class objects treated in the same way as functions provided by the system. S code is eminently readable and so a good way to document precisely what algorithms were used, and as much of the implementations are themselves written in S, they can be studied as models and to understand their subtleties. The current implementations also provide easy ways for S functions to call compiled code written in C, Fortran and similar languages; this is documented here in depth. Increasingly S is being used for statistical or graphical analysis within larger software systems or for whole vertical-market applications. The interface facilities are most developed on Windows® and these are covered with worked examples. The authors have written the widely used Modern Applied Statistics with S-PLUS, now in its third edition, and several software libraries that enhance S-PLUS and R; these and the examples used in both books are available on the Internet. Dr. W.N. Venables is a senior Statistician with the CSIRO/CMIS Environmetrics Project in Australia, having been at the Department of Statistics, University of Adelaide for many years previously. Professor B.D. Ripley holds the Chair of Applied Statistics at the University of Oxford, and is the author of four other books on spatial statistics, simulation, pattern recognition and neural networks. Both authors are known and respected throughout the international S and R communities, for their books, workshops, short courses, freely available software and through their extensive contributions to the S-news and R mailing lists.

Software for Data Analysis

Author: John Chambers
Publisher: Springer Science & Business Media
ISBN: 9780387759364
Format: PDF
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John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved.

Programming with Data

Author: John M. Chambers
Publisher: Springer Science & Business Media
ISBN: 9780387985039
Format: PDF
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Here is a thorough and authoritative guide to the latest version of the S language and to its programming environment the premier software platform for computing with data. Programming with Data describes a new and greatly extended version of S and is written by the chief designer of the language. The book is a guide to the complete programming process, starting from simple interactive use and continuing through ambitious software projects. S is designed for computing with data-for any project in which organizing, visualizing, summarizing, or modeling data are central concerns. Its focus is on the needs of the programmer/user, and its goal is "to turn ideas into software, quickly and faithfully." S is a functional object-based language with a huge library of functions for all aspects of computing with data. Its long and enthusiastic use in statistics and applied fields has also led to many valuable libraries of user-written functions. The new version of S provides powerful class/method structure, new techniques to deal with large objects, extended interfaces to other languages and files, object-based documentation compatible with HTML, and powerful new interactive programming techniques. This version of S underlies the S-PLUS system, versions 5*0 and higher.

Modern Applied Statistics with S PLUS

Author: William N. Venables
Publisher: Springer Science & Business Media
ISBN: 1475727194
Format: PDF, Kindle
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A guide to using the power of S-PLUS to perform statistical analyses, providing both an introduction to the program and a course in modern statistical methods. Readers are assumed to have a basic grounding in statistics, thus the book is intended for would-be users, as well as students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets, with many of the methods discussed being modern approaches to topics such as linear and non-linear regression models, robust and smooth regression methods, survival analysis, multivariate analysis, tree-based methods, time series, spatial statistics, and classification. This second edition is intended for users of S-PLUS 3.3, or later, and covers both Windows and UNIX. It treats the recent developments in graphics and new statistical functionality, including bootstraping, mixed effects linear and non-linear models, factor analysis, and regression with autocorrelated errors. The authors have written several software libraries which enhance S-PLUS, and these, plus all the datasets used, are available on the Internet.

Introductory Statistics with R

Author: Peter Dalgaard
Publisher: Springer Science & Business Media
ISBN: 0387790543
Format: PDF, Mobi
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This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

Statistical Computing in C and R

Author: Randall L. Eubank
Publisher: CRC Press
ISBN: 1420066501
Format: PDF, ePub, Docs
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With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a "boot-camp" on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. Features Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors’ website The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment.

The New S Language

Author: R. Becker
Publisher: CRC Press
ISBN: 1351091883
Format: PDF, ePub, Docs
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This book provides documentation for a new version of the S system released in 1988. The new S enhances the features that have made S popular: interactive computing, flexible graphics, data management and a large collection of functions. The new S features make possible new applications and higher-level programming, including a single unified language, user defined functions as first-class objects, symbolic computations, more accurate numerical calculations and a new approach to graphics. S now provides direct interfaces to the poowerful tool of the UNIX operating system and to algorithms implemented in Fortran and C.

Statistical Models in S

Author: T.J. Hastie
Publisher: Routledge
ISBN: 1351414224
Format: PDF, Docs
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Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.

The Art of R Programming

Author: Norman Matloff
Publisher: No Starch Press
ISBN: 1593273843
Format: PDF, ePub, Docs
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A guide to software development using the R programming language covers such topics as closures, recursion, anonymous functions, and debugging techniques.