### Features

- Shows how to obtain informative graphical output using R
- Provides R code so readers can perform their own analyses
- Emphasizes the practical application and interpretation of results rather than focusing on the theory behind the analyses
- Offers an introduction to R, including a summary of its most important features
- Contains many examples and exercises

### Summary

*A Proven Guide for Easily Using R to Effectively Analyze Data*

Like its bestselling predecessor, **A Handbook of Statistical Analyses Using R, Second Edition** provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references.

**New to the Second Edition**

- New chapters on graphical displays, generalized additive models, and simultaneous inference
- A new section on generalized linear mixed models that completes the discussion on the analysis of longitudinal data where the response variable does not have a normal distribution
- New examples and additional exercises in several chapters
- A new version of the HSAUR package (HSAUR2), which is available from CRAN

This edition continues to offer straightforward descriptions of how to conduct a range of statistical analyses using R, from simple inference to recursive partitioning to cluster analysis. Focusing on how to use R and interpret the results, it provides students and researchers in many disciplines with a self-contained means of using R to analyze their data.

### Table of Contents

**An Introduction to R**

What Is R?

Installing R

Help and Documentation

Data Objects in R

Data Import and Export

Basic Data Manipulation

Computing with Data

Organizing an Analysis

**Data Analysis Using Graphical Displays**

Introduction

Initial Data Analysis

Analysis Using R

**Simple Inference**

Introduction

Statistical Tests

Analysis Using R

**Conditional Inference**

Introduction

Conditional Test Procedures

Analysis Using R

**Analysis of Variance**

Introduction

Analysis of Variance

Analysis Using R

**Simple and Multiple Linear Regression**

Introduction

Simple Linear Regression

Multiple Linear Regression

Analysis Using R

**Logistic Regression and Generalized Linear Models**

Introduction

Logistic Regression and Generalized Linear Models

Analysis Using R

**Density Estimation**

Introduction

Density Estimation

Analysis Using R

**Recursive Partitioning**

Introduction

Recursive Partitioning

Analysis Using R

**Scatterplot Smoothers and Generalized Additive Models**

Introduction

Scatterplot Smoothers and Generalized Additive Models

Analysis Using R

**Survival Analysis**

Introduction

Survival Analysis

Analysis Using R

**Analyzing Longitudinal Data I**

Introduction

Analyzing Longitudinal Data

Linear Mixed Effects Models

Analysis Using R

Prediction of Random Effects

The Problem of Dropouts

**Analyzing Longitudinal Data II**

Introduction

Methods for Nonnormal Distributions

Analysis Using R: GEE

Analysis Using R: Random Effects

**Simultaneous Inference and Multiple Comparisons**

Introduction

Simultaneous Inference and Multiple Comparisons

Analysis Using R

**Meta-Analysis**

Introduction

Systematic Reviews and Meta-Analysis

Statistics of Meta-Analysis

Analysis Using R

Meta-Regression

Publication Bias

**Principal Component Analysis**

Introduction

Principal Component Analysis

Analysis Using R

**Multidimensional Scaling**

Introduction

Multidimensional Scaling

Analysis Using R

**Cluster Analysis**

Introduction

Cluster Analysis

Analysis Using R

**Bibliography **

**Index**

*A Summary appears at the end of each chapter.*

### Author(s) Bio

**Brian S. Everitt** is Professor Emeritus at King’s College, University of London.

**Torsten Hothorn** is Professor of Biostatistics in the Institut für Statistik at Ludwig-Maximilians-Universität München.

### Reviews

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

**Praise for the First Edition**

…Brian Everitt has joined forces with a recognized expert who displays an impressive command of this powerful environment … Much is to be learned in the small details that make this text interesting even for experienced users. … Special attention is given to graphical methods …

—*Journal of Applied Statistics*, May 2007

Useful examples are presented to assist understanding. … Everitt and Hothorn have written an excellent tutorial on using R to analyze data using a wide range of standard statistical methods. … I highly recommend the text for anyone learning R and who want to use it for the sophisticated analysis of data.

—Joseph M. Hilbe, *Journal of Statistical Software*, Vol. 16, August 2006

…a useful, compact introduction.

—*Biometrics*, December 2006

… This book, using analyses of real sets of data, takes the reader through many of the standard forms of statistical methodology using R. … a very valuable reference. …The book is particularly good at highlighting the graphical capabilities of the language. …

—P. Marriott, *ISI Short Book Reviews*