Kleinman K., Horton N.J. SAS and R: Data Management, Statistical Analysis and Graphics

CRC Press, 2010. — 302 p. — ISBN: 9781420070576Provides 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 analyzes that employ a single data set from the HELP study.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 Analyzes of the HELP Data Set
Detailed Description of the Data Set
Appendix D: References
Appendix E: IndicesSAS Index
R Index