3rd Edition
A Handbook of Statistical Analyses using R
Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.
New to the Third Edition
- Three new chapters on quantile regression, missing values, and Bayesian inference
- Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables
- Additional exercises
- More detailed explanations of R code
- New section in each chapter summarizing the results of the analyses
- Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses
Whether you’re a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.
Introduction
Density Estimation
Analysis Using R
Summary of Findings
Final Comments
Recursive Partitioning
Introduction
Recursive Partitioning
Analysis Using R
Summary of Findings
Final Comments
Scatterplot Smoothers and Additive Models
Introduction
Scatterplot Smoothers and Generalised Additive Models
Analysis Using R
Summary of Findings
Final Comments
Survival Analysis
Introduction
Survival Analysis
Analysis Using R
Summary of Findings
Final Comments
Quantile Regression
Introduction
Quantile Regression
Analysis Using R
Summary of Findings
Final Comments
Analysing Longitudinal Data I
Introduction
Analysing Longitudinal Data
Linear Mixed Effects Models
Analysis Using R
Prediction of Random Effects
The Problem of Dropouts
Summary of Findings
Final Comments
Analysing Longitudinal Data II
Introduction
Methods for Non-Normal Distributions
Analysis Using R: GEE
Analysis Using R: Random Effects
Summary of Findings
Final Comments
Simultaneous Inference and Multiple Comparisons
Introduction
Simultaneous Inference and Multiple Comparisons
Analysis Using R
Summary of Findings
Final Comments
Missing Values
Introduction
The Problems of Missing Data
Dealing with Missing Values
Imputing Missing Values
Analyzing Multiply Imputed Data
Analysis Using R
Summary of Findings
Final Comments
Meta-Analysis
Introduction
Systematic Reviews and Meta-Analysis
Statistics of Meta-Analysis
Analysis Using R
Meta-Regression
Publication Bias
Summary of Findings
Final Comments
Bayesian Inference
Introduction
Bayesian Inference
Analysis Using R
Summary of Findings
Final Comments
Principal Component Analysis
Introduction
Principal Component Analysis
Analysis Using R
Summary of Findings
Final Comments
Multidimensional Scaling
Introduction
Multidimensional Scaling
Analysis Using R
Summary of Findings
Final Comments
Cluster Analysis
Introduction
Cluster Analysis
Analysis Using R
Summary of Findings
Final Comments
Bibliography
Index
Biography
Torsten Hothorn, Brian S. Everitt
“I truly appreciate how grounded in practicality this book is—and the way its chapters are structured really underlines this. Furthermore, all the datasets are interesting and vary widely in subject matter. If nothing else, this book is an excellent source of examples one might use to illustrate a variety of statistical techniques. … it offers a lot of good places to start if one wants to analyze data. … The book comes hand-in-hand with an R package, HSAUR3, with all the data and the code used in the text. The book is thus fully reproducible. Overall, it provides a great way for a statistician to get started doing a wide variety of things in the R environment. It would be particularly useful, then, for working statisticians looking to change their software. The book cites all the relevant packages one might need, which is quite nice for those attempting to navigate the vast array of packages freely available, and is quite clear in its presentation of the code. Between this and the datasets, it makes for quite a valuable and enjoyable reference.”
—The American Statistician, August 2015"… a handy primer for using R to perform standard statistical data analysis. … students, analysts, professors, and scientists: if you are looking to add R to your toolkit for analyzing data statistically, then this book will get you there."
—Kendall Giles on his blog, September 2014Praise for the Second Edition:
"I find the book by Everitt and Hothorn quite pleasant and bound to fit its purpose. The layout and presentation [are] nice. It should appeal to all readers as it contains a wealth of information about the use of R for statistical analysis. Included seasoned R users: When reading the first chapters, I found myself scribbling small lightbulbs in the margin to point out features of R I was not aware of. In addition, the book is quite handy for a crash introduction to statistics for (well-enough motivated) nonstatisticians."
—International Statistical Review (2011), 79"… an extensive selection of real data analyzed with [R] … Viewed as a collection of worked examples, this book has much to recommend it. Each chapter addresses a specific technique. … the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study. … This handbook is unusually free of the sort of errors spell checkers do not find."
—MAA Reviews, April 2011
