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Handbook of statistics : computational statistics with R /
R is open source statistical computing software. Since the R core group was formed in 1997, R has been extended by a very large number of packages with extensive documentation along with examples freely available on the internet. It offers a large number of statistical and numerical methods and grap...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Elsevier,
2014.
|
| Colección: | Handbook of statistics (Amsterdam, Netherlands) ;
v. 32. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Front Cover; Computational Statistics with R; Copyright; Contents; Contributors; Preface; Chapter 1: Introduction to R; Chapter 2: R Graphics; Chapter 3: Graphics Miscellanea; Chapter 4: Matrix Algebra Topics in Statistics and Economics Using R; Chapter 5: Sample Size Calculations with R: Level 1; Chapter 6: Sample Size Calculations with R: Level 2; Chapter 7: Binomial Regression in R; Chapter 8: Computing Tolerance Intervals and Regions Using R; Chapter 9: Modeling the Probability of Second Cancer in Controlled Clinical Trials; Chapter 10: Bayesian Networks; References.
- Chapter 1: Introduction to R1. Introduction; 2. Setting Up R; 2.1. Installing and Starting R; 2.2. Memory; 2.3. Saving Your Code and Workspace; 2.4. R Packages; 3. Basic R Objects and Commands; 3.1. Numbers, Character Strings, and Logicals; 3.2. Scalars, Vectors, Matrices, and Arrays; 3.3. Data Frames and Lists; 3.4. Strings and Factors; 4. Writing Programs; 4.1. Conditional Statements; 4.2. if/else Statements; 4.3. for Loops; 4.4. while Loops; 4.5. Functions; 4.6. Debugging and Efficiency; 5. Input and Output; 6. Data Processing; 7. Exploratory Data Analysis.
- 8. Statistical Inference and Modeling8.1. Hypothesis Testing; 8.2. Regression; 9. Simulation; 10. Numerical Techniques; 11. Annotated References; Set Up; Text Editors; Introductory Resources and Books; Chapter 2: R Graphics; 1. Introduction; 1.1. Origins; 1.2. Principles of Data Graphics; 2. Traditional Graphics; 2.1. The plot() Function; 2.2. Other Common High-Level Functions; 2.3. Visualizations for Time Series Data; 2.4. Customizing Plots Using Low-Level Functions; 2.5. Limitations of Traditional Graphics; 3. Grid Graphics; 3.1. Viewports; 3.2. Units and Primitives; 3.3. First Attempt.
- 4. Lattice4.1. Overview; 4.2. Common High-Level Functions; 4.3. Bar Charts and Dot Plots for Tabular Data; 4.4. Scatterplots and Custom Displays; 4.5. The ��trellis�� Object; 5. ggplot; 6. Further Reading; References; Chapter 3: Graphics Miscellanea; 1. Introduction; 2. The Plot( ) Command; 2.1. Features that Can Be Included in a Scatter Plot; 2.1.1. par( ) Command; 3. Scatter Plots; 3.1. Regression Analysis with Scatter Plots; 3.2. Multiple Regression Analysis with Scatterplot Matrices; 3.3. Scatterplot Matrices of Data Segregated by a Categorical Variable; 4. Time Series Plots.
- 4.1. Three Graphs in a Single Frame4.2. Two Different Time Series Data Sets in a Single Plot; 5. Pie Charts; 6. Special Box Plots; 7. xy Plots; 8. Curves; 9. LOWESS; 10. Sunflower Plots; 11. Violin Plots; 12. Bean Plots; 13. Bubble Charts; 14. 3D Surface Plot; 15. Chernoff Faces-Graphical Presentation of Multivariate Data; 16. Maps; 16.1. Drawing Common Maps; 16.2. Creating a Choropleth Map; 16.2.1. Creating Maps with Custom Colors Depending on Values; References; Chapter 4: Matrix Algebra Topics in Statistics and Economics Using R; 1. Introduction; 2. Basic Matrix Manipulations in R.