Overview

An All-in-One Resource for Using SAS and R to Carry out Common Tasks Provides a path between languages that is easier than reading complete documentation SAS and R: Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and the creation of graphics, along with more complex applications. Takes an innovative, easy-to-understand, dictionary-like approach Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The book enables easier mobility between the two systems: SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Demonstrating the code in action and facilitating exploration, the authors present extensive example analyses that employ a single data set from the HELP study. They offer the data sets and code for download on the book’s website.

Full Product Details

Author:   Ken Kleinman ,  Nicholas J. Horton (Amherst College, Amherst, MA)
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Dimensions:   Width: 17.40cm , Height: 2.30cm , Length: 24.60cm
Weight:   0.794kg
ISBN:  

9781420070576

ISBN 10:   1420070576
Pages:   343
Publication Date:   22 July 2009
Audience:   Professional and scholarly ,  Professional and scholarly ,  Professional & Vocational ,  Professional & Vocational
Replaced By:   9781466584495
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

Data Management Input Output Structure and Meta-Data Derived Variables and Data Manipulation Merging, Combining, and Subsetting Data Sets Date and Time Variables Interactions with the Operating System Mathematical Functions Matrix Operations Probability Distributions and Random Number Generation Control Flow, Programming, and Data Generation Common Statistical Procedures Summary Statistics Bivariate Statistics Contingency Tables Two Sample Tests for Continuous Variables Linear Regression and ANOVA Model Fitting Model Comparison and Selection Tests, Contrasts, and Linear Functions of Parameters Model Diagnostics Model Parameters and Results Regression Generalizations Generalized Linear Models Models for Correlated Data Survival Analysis Further Generalizations to Regression Models Graphics A Compendium of Useful Plots Adding Elements Options and Parameters Saving Graphs Other Topics and Extended Examples Power and Sample Size Calculations Generate Data from Generalized Linear Random Effects Model Generate Correlated Binary Data Read Variable Format Files and Plot Maps Missing Data: Multiple Imputation Bayesian Poisson Regression Multivariate Statistics and Discriminant Procedures Complex Survey Design Appendix A: Introduction to SAS Installation Running SAS and a Sample Session Learning SAS and Getting Help Fundamental Structures: Data Step, Procedures, and Global Statements Work Process: The Cognitive Style of SAS Useful SAS Background Accessing and Controlling SAS Output: The Output Delivery System The SAS Macro Facility: Writing Functions and Passing Values Miscellanea Appendix B: Introduction to R Installation Running R and Sample Session Learning R and Getting Help Fundamental Structures: Objects, Classes, and Related Concepts Built-in and User-Defined Functions Add-ons: Libraries and Packages Support and Bugs Appendix C: The HELP Study Data Set Background on the HELP Study Roadmap to Analyses of the HELP Data Set Detailed Description of the Data Set Appendix D: References Appendix E: Indices Subject Index SAS Index R Index Further Resources and HELP Examples appear at the end of each chapter.

Reviews

It is an excellent text that is designed to translate SAS to R. ! For statisticians with knowledge of both SAS and R programming, this book provides a useful resource to understand the differences between SAS and R codes and can be used for browsing and for finding particular SAS and R functions to perform common tasks. The book will strengthen the analytical abilities of relatively new users of either system by providing them with a concise reference manual and annotated examples executed in both packages. Professional analysts as well as statisticians, epidemiologists and others who are engaged in research or data analysis will find this book very useful. The book is comprehensive and covers an extensive list of statistical techniques from data management to graphics procedures, cross-referencing, indexing and good worked examples in SAS and R at the end of each chapter. --Significance, July 2011 ! a convenient reference text to quickly learn by example how to perform common tasks in both software packages. ! the book provides a powerful starting point to a wide variety of statistical techniques available in SAS and R. ! it facilitates a translation between SAS and R, without getting overly detailed or technical. It is mainly useful as a starting point for those who already know either R or SAS, and want to learn the other language, without going over extensive manuals or introductory texts. --Journal of Statistical Software, January 2011, Volume 37

! a convenient reference text to quickly learn by example how to perform common tasks in both software packages. ! the book provides a powerful starting point to a wide variety of statistical techniques available in SAS and R. ! it facilitates a translation between SAS and R, without getting overly detailed or technical. It is mainly useful as a starting point for those who already know either R or SAS, and want to learn the other language, without going over extensive manuals or introductory texts. --Journal of Statistical Software, January 2011, Volume 37

It is clearly written and code is appropriately highlighted to facilitate readability. ! it is a potentially useful reference material for experienced users of one of the two systems, who need to quickly find how to perform a familiar task in the alternative system. --Biometrics, 67, September 2011 It is an excellent text that is designed to translate SAS to R. ! For statisticians with knowledge of both SAS and R programming, this book provides a useful resource to understand the differences between SAS and R codes and can be used for browsing and for finding particular SAS and R functions to perform common tasks. The book will strengthen the analytical abilities of relatively new users of either system by providing them with a concise reference manual and annotated examples executed in both packages. Professional analysts as well as statisticians, epidemiologists and others who are engaged in research or data analysis will find this book very useful. The book is comprehensive and covers an extensive list of statistical techniques from data management to graphics procedures, cross-referencing, indexing and good worked examples in SAS and R at the end of each chapter. --Significance, July 2011 ! a convenient reference text to quickly learn by example how to perform common tasks in both software packages. ! the book provides a powerful starting point to a wide variety of statistical techniques available in SAS and R. ! it facilitates a translation between SAS and R, without getting overly detailed or technical. It is mainly useful as a starting point for those who already know either R or SAS, and want to learn the other language, without going over extensive manuals or introductory texts. --Journal of Statistical Software, January 2011, Volume 37

Author Information

Ken Kleinman is an associate professor at Harvard Medical School. His research deals with clustered data analysis, surveillance, and epidemiological applications. Nicholas J. Horton is an associate professor of statistics at Smith College. His research interests include longitudinal regression models and missing data methods.

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