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Computational Statistics with R - 1st Edition - ISBN: 9780444634313, 9780444634412

Computational Statistics with R, Volume 32

1st Edition

Series Volume Editors: Marepalli Rao C.R. Rao
Hardcover ISBN: 9780444634313
eBook ISBN: 9780444634412
Imprint: Elsevier
Published Date: 25th November 2014
Page Count: 412
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Table of Contents

  • 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
  • Chapter 1: Introduction to R
    • Abstract
    • 1 Introduction
    • 2 Setting Up R
    • 3 Basic R Objects and Commands
    • 4 Writing Programs
    • 5 Input and Output
    • 6 Data Processing
    • 7 Exploratory Data Analysis
    • 8 Statistical Inference and Modeling
    • 9 Simulation
    • 10 Numerical Techniques
    • 11 Annotated References
  • Chapter 2: R Graphics
    • Abstract
    • 1 Introduction
    • 2 Traditional Graphics
    • 3 Grid Graphics
    • 4 Lattice
    • 5 ggplot
    • 6 Further Reading
  • Chapter 3: Graphics Miscellanea
    • Abstract
    • 1 Introduction
    • 2 The Plot() Command
    • 3 Scatter Plots
    • 4 Time Series Plots
    • 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
  • Chapter 4: Matrix Algebra Topics in Statistics and Economics Using R
    • Abstract
    • 1 Introduction
    • 2 Basic Matrix Manipulations in R
    • 3 Descriptive Statistics
    • 4 Matrix Transformations, Invariance, and Equivariance
    • 5 Payoff Matrices in Decision Analysis
    • 6 Matrix Algebra in Regression Models
    • 7 Correlation Matrices and Generalizations
    • 8 Matrices for Population Dynamics
    • 9 Multivariate Components Analysis
    • 10 Sparse Matrices
  • Chapter 5: Sample Size Calculations with R: Level 1
    • Abstract
    • 1 Introduction
    • 2 General Ideas on Sample Size Calculations
    • 3 Single-Sample Problems
    • 4 Two-Sample Problems: Quantitative Responses
    • 5 Multisample Problem—Quantitative Responses—Analysis of Variance
  • Chapter 6: Sample Size Calculations with R: Level 2
    • Abstract
    • 1 Single Proportions
    • 2 Two-Sample Proportions
    • 3 Effect Sizes
    • 4 Multisample Proportions
    • 5 McNemar Test
    • 6 Correlations
    • 7 Hazard Ratio in Survival Analysis
    • 8 Multiple Regression
  • Chapter 7: Binomial Regression in R
    • Abstract
    • 1 Binomial Regression in the Generalized Linear Model
    • 2 Standard Logistic Regression
    • 3 Assumptions Involved in the Standard Logistic Regression Model
    • 4 Residuals
    • 5 Overdispersion
    • 6 Hypothesis Testing and Inference
    • 7 Model Performance
    • 8 Modeling Repeated (Longitudinal) Binary Measures
    • 9 Model Selection
    • 10 Machine Learning Methods
    • 11 Concluding Remarks
  • Chapter 8: Computing Tolerance Intervals and Regions Using R
    • Abstract
    • 1 Introduction
    • 2 Tolerance Intervals for Continuous Distributions
    • 3 Tolerance Intervals for Discrete Distributions
    • 4 Nonparametric Tolerance Intervals
    • 5 Regression Tolerance Intervals
    • 6 Multivariate Tolerance Regions
    • 7 Final Remarks
  • Chapter 9: Modeling the Probability of Second Cancer in Controlled Clinical Trials
    • Abstract
    • 1 Introduction
    • 2 Difficulties in Second Cancer Research
    • 3 Current Knowledge of Second Malignancy
    • 4 Clinical Trial Database
    • 5 Integrated Analysis
    • 6 Assessing Model Adequacy
    • 7 Summary
  • Chapter 10: Bayesian Networks
    • Abstract
    • 1 Introduction
    • 2 Joint and Conditional Distributions
    • 3 Generalities and Issues
    • 4 Graph Theory
    • 5 A Case Study
    • 6 Network Model Fitting
    • 7 Learning Algorithm
  • Subject Index


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 graphical tools and visualization of extraordinarily high quality. R was recently ranked in 14th place by the Transparent Language Popularity Index and 6th as a scripting language, after PHP, Python, and Perl.

The book is designed so that it can be used right away by novices while appealing to experienced users as well. Each article begins with a data example that can be downloaded directly from the R website. Data analysis questions are articulated following the presentation of the data. The necessary R commands are spelled out and executed and the output is presented and discussed. Other examples of data sets with a different flavor and different set of commands but following the theme of the article are presented as well. Each chapter predents a hands-on-experience. R has superb graphical outlays and the book brings out the essentials in this arena. The end user can benefit immensely by applying the graphics to enhance research findings. The core statistical methodologies such as regression, survival analysis, and discrete data are all covered.

Key Features

  • Addresses data examples that can be downloaded directly from the R website
  • No other source is needed to gain practical experience
  • Focus on the essentials in graphical outlays


Teachers of statistics, students, statistical consultants, statisticians and biostatisticians in industry


No. of pages:
© Elsevier 2014
25th November 2014
Hardcover ISBN:
eBook ISBN:


"This volume and the previous volumes in the Handbook of Statistics series will certainly constitute a valuable aid for a large number of researchers in different fields of science...a valuable guide to computer scientists, statistical consultants, computer programmers and research workers in all fields involved in data analysis." --Zentralblatt MATH

Ratings and Reviews

About the Series Volume Editors

Marepalli Rao

Affiliations and Expertise

UC College of Medicine, Cincinnati, OH, USA

C.R. Rao

C.R. Rao

Professor C. R. Rao, born in India, is one of this century's foremost statisticians, and received his education in statistics at the Indian Statistical Institute (ISI), Calcutta. He is Emeritus Holder of the Eberly Family Chair in Statistics at Penn State and Director of the Center for Multivariate Analysis. He has long been recognized as one of the world's top statisticians, and has been awarded 34 honorary doctorates from universities in 19 countries spanning 6 continents. His research has influenced not only statistics, but also the physical, social and natural sciences and engineering.

In 2011 he was recipient of the Royal Statistical Society's Guy Medal in Gold which is awarded triennially to those "who are judged to have merited a signal mark of distinction by reason of their innovative contributions to the theory or application of statistics". It can be awarded both to fellows (members) of the Society and to non-fellows. Since its inception 120 years ago the Gold Medal has been awarded to 34 distinguished statisticians. The first medal was awarded to Charles Booth in 1892. Only two statisticians, H. Cramer (Norwegian) and J. Neyman (Polish), outside Great Britain were awarded the Gold medal and C. R. Rao is the first non-European and non-American to receive the award.

Other awards he has received are the Gold Medal of Calcutta University, Wilks Medal of the American Statistical Association, Wilks Army Medal, Guy Medal in Silver of the Royal Statistical Society (UK), Megnadh Saha Medal and Srinivasa Ramanujan Medal of the Indian National Science Academy, J.C.Bose Gold Medal of Bose Institute and Mahalanobis Centenary Gold Medal of the Indian Science Congress, the Bhatnagar award of the Council of Scientific and Industrial Research, India and the Government of India honored him with the second highest civilian award, Padma Vibhushan, for “outstanding contributions to Science and Engineering / Statistics”, and also instituted a cash award in honor of C R Rao, “to be given once in two years to a young statistician for work done during the preceding 3 years in any field of statistics”.

For his outstanding achievements Rao has been honored with the establishment of an institute named after him, C.R.Rao Advanced Institute for Mathematics, Statistics and Computer Science, in the campus of the University of Hyderabad, India.

Affiliations and Expertise

The Pennsylvania State University, University Park, PA, USA