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SAS and R : data management, statistical analysis, and graphics / Ken Kleinman, Nicholas J. Horton.

By: Kleinman, Ken.
Contributor(s): Horton, Nicholas J.
Material type: TextTextPublisher: Boca Raton : CRC Press, c2010Description: xix, 323 p. : ill., map ; 27 cm.ISBN: 9781420070576 (hard back : alk. paper); 1420070576 (hard back : alk. paper).Subject(s): SAS (Computer program language) | R (Computer program language)DDC classification: 610.72 KLS Other classification: 54.50 Online resources: OCLC | Ebook Fulltext
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.
Summary: Presents a different way to learn how to perform an analytical task in both SAS and R. This book covers many common tasks, along with more complex applications. It provides parallel examples in SAS and R to demonstrate how to use the software and derive identical answers regardless of software choice.
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"A Chapman & Hall book."

Includes bibliographical references and indexes.

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.

Presents a different way to learn how to perform an analytical task in both SAS and R. This book covers many common tasks, along with more complex applications. It provides parallel examples in SAS and R to demonstrate how to use the software and derive identical answers regardless of software choice.

Applied Statistics

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