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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010277632 | QA76.73.S27 K544 2010 | Open Access Book | Book | Searching... |
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Summary
Summary
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 Graphicspresents 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.
Author Notes
Ken Kleinmanis an associate professor at Harvard Medical School. His research deals with clustered data analysis, surveillance, and epidemiological applications.
Nicholas J. Hortonis an associate professor of statistics at Smith College. His research interests include longitudinal regression models and missing data methods.
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 |
