Computational statistics : an introduction to R

Title:

Personal Author:

Sawitzki, Gunther

Publication Information:

Boca Raton, FL : Chapman & Hall, 2009

Physical Description:

xiv, 251 p. : ill. (some col.) ; 25 cm.

ISBN:

9781420086782

Subject Term:

R (Computer program language)

Mathematical statistics -- Data processing

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010226342	QA276.45.R3 S39 2009	Open Access Book	Book	Searching... Unknown
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Suitable for a compact course or self-study, Computational Statistics: An Introduction to R illustrates how to use the freely available R software package for data analysis, statistical programming, and graphics. Integrating R code and examples throughout, the text only requires basic knowledge of statistics and computing.

This introduction covers one-sample analysis and distribution diagnostics, regression, two-sample problems and comparison of distributions, and multivariate analysis. It uses a range of examples to demonstrate how R can be employed to tackle statistical problems. In addition, the handy appendix includes a collection of R language elements and functions, serving as a quick reference and starting point to access the rich information that comes bundled with R.

Accessible to a broad audience, this book explores key topics in data analysis, regression, statistical distributions, and multivariate statistics. Full of examples and with a color insert, it helps readers become familiar with R.

Reviews 1

Choice Review

This book provides an introduction to the free programming language and software system R, which is widely used for statistical applications. In the introduction, Sawitzki (StatLab, Heidelberg, Germany) mentions that the work is for users who have some background knowledge of statistics, including such concepts as distribution functions, mean, and variance, and some knowledge of classical distributions, e.g., binomial, normal, etc. The volume consists of four parts: "Basic Data Analysis," "Regression," "Comparisons" (of treatments/distributions), and "Dimensions 1, 2, 3, ..., (infinity)" (analysis of multivariate data). A special feature of the book is each section's concluding, one-page summary of its contents. The author provides a solid introduction to R, and discusses a variety of uses of R for statistical analysis, with an abundance of examples showing how to use the language. Computational Statistics definitely deserves to be in the library of all institutions where R is used or taught. Summing Up: Highly recommended. All collections. R. Bharath emeritus, Northern Michigan University

Introduction	p. v
1 Basic Data Analysis	p. 1
1.1 R Programming Conventions	p. 1
1.2 Generation of Random Numbers and Patterns	p. 4
1.2.1 Random Numbers	p. 4
1.2.2 Patterns	p. 9
1.3 Case Study: Distribution Diagnostics	p. 10
1.3.1 Distribution Functions	p. 13
1.3.2 Histograms	p. 17
Barcharts	p. 21
1.3.3 Statistics of Distribution Functions: Kolmogorov-Smirnov Tests	p. 22
Monte Carlo Confidence Bands	p. 23
1.3.4 Statistics of Histograms and Related Plots; X2-Tests	p. 29
1.4 Moments and Quantiles	p. 34
1.5 R Complements	p. 39
1.5.1 Random Numbers	p. 39
1.5.2 Graphical Comparisons	p. 40
1.5.3 Functions	p. 46
1.5.4 Enhancing Graphical Displays	p. 50
1.5.5 R Internals	p. 53
parse	p. 53
eval	p. 53
print	p. 54
Executing Files	p. 54
1.5.6 Packages	p. 54
1.6 Statistical Summary	p. 56
1.7 Literature and Additional References	p. 57
2 Regression	p. 59
2.1 General Regression Model	p. 59
2.2 Linear Model	p. 60
2.2.1 Factors	p. 63
2.2.2 Least Squares Estimation	p. 64
2.2.3 Regression Diagnostics	p. 69
2.2.4 More Examples for Linear Models	p. 75
2.2.5 Model Formulae	p. 76
2.2.6 Gauss-Markov Estimator and Residuals	p. 77
2.3 Variance Decomposition and Analysis of Variance	p. 79
2.4 Simultaneous Inference	p. 85
2.4.1 Scheffé's Confidence Bands	p. 85
2.4.2 Tukey's Confidence Intervals	p. 87
Case Study: Titre Plates	p. 88
2.5 Beyond Linear Regression	p. 96
Transformations	p. 96
2.5.1 Generalised Linear Models	p. 96
2.5.2 Local Regression	p. 97
2.6 R Complements	p. 101
2.6.1 Discretisation	p. 101
2.6.2 External Data	p. 101
2.6.3 Testing Software	p. 101
2.6.4 R Data Types	p. 102
2.6.5 Classes and Polymorphic Functions	p. 103
2.6.6 Extractor Functions	p. 104
2.7 Statistical Summary	p. 105
2.8 Literature and Additional References	p. 105
3 Comparisons	p. 107
3.1 Shift/Scale Families, and Stochastic Order	p. 109
3.2 QQ Plot, PP Plot, and Comparison of Distributions	p. 111
3.2.1 Kolmogorov-Smirnov Tests	p. 116
3.3 Tests for Shift Alternatives	p. 117
3.4 A Road Map	p. 125
3.5 Power and Confidence	p. 126
3.5.1 Theoretical Power and Confidence	p. 126
3.5.2 Simulated Power and Confidence	p. 130
3.5.3 Quantile Estimation	p. 133
3.6 Qualitative Features of Distributions	p. 135
3.7 Statistical Summary	p. 136
3.8 Literature and Additional References	p. 137
4 Dimensions 1, 2, 3, ..., ¿	p. 139
4.1 R Complements	p. 140
4.2 Dimensions	p. 143
4.3 Selections	p. 145
4.4 Projections	p. 145
4.4.1 Marginal Distributions and Scatter Plot Matrices	p. 145
4.4.2 Projection Pursuit	p. 150
4.4.3 Projections for Dimensions 1, 2, 3, ...7	p. 153
4.4.4 Parallel Coordinates	p. 154
4.5 Sections, Conditional Distributions and Coplots	p. 156
4.6 Transformation and Dimension Reduction	p. 162
4.7 Higher Dimensions	p. 167
4.7.1 Linear Case	p. 167
Partial Residuals and Added Variable Plots	p. 168
4.7.2 Non-Linear Case	p. 169
Example: Cusp Non-Linearity	p. 169
4.7.3 Case Study: Melbourne Temperature Data	p. 173
4.7.4 Curse of Dimensionality	p. 174
4.7.5 Case Study: Body Fat	p. 175
4.8 High Dimensions	p. 189
4.9 Statistical Summary	p. 190
R as a Programming Language and Environment	p. 193
A.1 Help and Information	p. 193
A.2 Names and Search Paths	p. 195
A.3 Administration and Customisation	p. 196
A.4 Basic Data Types	p. 197
A.5 Output for Objects	p. 199
A.6 Object Inspection	p. 200
A.7 System Inspection	p. 201
A.8 Complex Data Types	p. 202
A.9 Accessing Components	p. 204
A.10 Data Manipulation	p. 206
A.11 Operators	p. 208
A.12 Functions	p. 209
A.13 Debugging and Profiling	p. 211
A.14 Control Structures	p. 213
A.15 Input and Output to Data Streams; External Data	p. 215
A.16 Libraries, Packages	p. 218
A.17 Mathematical Operators and Functions; Linear Algebra	p. 220
A.18 Model Descriptions	p. 221
A.19 Graphic Functions	p. 223
A.19.1 High-Level Graphics	p. 223
A.19.2 Low-Level Graphics	p. 224
A.19.3 Annotations and Legends	p. 225
A.19.4 Graphic Parameters and Layout	p. 226
A.20 Elementary Statistical Functions	p. 227
A.21 Distributions, Random Numbers, Densities...	p. 228
A.22 Computing on the Language	p. 231
References	p. 233
Functions and Variables by Topic	p. 237
Function and Variable Index	p. 245
Subject Index	p. 249

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