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A Handbook of Statistical Analyses Using R, Second Edition

By Torsten Hothorn, Brian S. Everitt

Chapman and Hall/CRC – 2009 – 376 pages

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    978-1-42-007933-3
    July 20th 2009

Description

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.

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

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 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.

Name: A Handbook of Statistical Analyses Using R, Second Edition (Paperback)Chapman and Hall/CRC 
Description: By Torsten Hothorn, Brian S. Everitt. 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...
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