# A Handbook of Statistical Analyses Using R

#### By **Torsten Hothorn**, **Brian S. Everitt**

Chapman and Hall/CRC – 2006 – 304 pages

Chapman and Hall/CRC – 2006 – 304 pages

R is dynamic, to say the least. More precisely, it is organic, with new functionality and add-on packages appearing constantly. And because of its open-source nature and free availability, R is quickly becoming the software of choice for statistical analysis in a variety of fields.

Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary. They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while summarizing the most important features. Numerous figures highlight R's strong graphical capabilities and exercises at the end of each chapter reinforce the techniques and concepts presented. All data sets and code used in the book are available as a downloadable package from CRAN, the R online archive.

A Handbook of Statistical Analyses Using R is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.

"…Brian Everitt has joined forces with a recognised 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 and this particular feature (which is one of R's qualities) has given the reviewer much pleasure and excitement. …"

-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. They use numerous examples throughout the text, present 100 figures, and show 54 tables to augment discussion. All this is done in a book of only 275 pages in length. I highly recommend the text for anyone learning R, and who want to use it for the sophisticated analysis of data."

-Joseph M. Hilbe, Emeritus Professor, University of Hawaii and Adjunct Professor, Sociology and Statistics, Arizona State University, Journal of Statistical Software, Vol. 16, August 2006

"…The book is clearly meant to help a true beginner get started with the R package. It begins appropriately with a chapter presenting a description of R and installation instructions, the help (simple help) and vignette (detailed help) commands, and other available documentation. This chapter also discusses basic data handling techniques and methods for summarizing data. The remainder of the book consists of 14 chapters, each of which describes a different type of analysis. … The chapters are generally well laid out and easy to understand. The book covers ANOVA/MANOVA, several forms of regression, an assortment of multivariate analyses, and various other forms of statistical analysis. … For the experienced analyst wanting to learn R, this book is 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 (University of Waterloo, Canada), Short Book Reviews

AN INTRODUCTION TO R

What Is R?

Installing R

Help and Documentation

Data Objects in R

Data Import and Export

Basic Data Manipulation

Simple Summary Statistics

Organising an Analysis

Summary

SIMPLE INFERENCE

Introduction

Statistical Tests

Analysis Using R

Summary

CONDITIONAL INFERENCE

Introduction

Conditional Test Procedures

Analysis Using R

Summary

ANALYSIS OF VARIANCE

Introduction

Analysis of Variance

Analysis Using R

Summary

MULTIPLE LINEAR REGRESSION

Introduction

Multiple Linear Regression

Analysis Using R

Summary

LOGISTIC REGRESSION AND GENERALISED LINEAR MODELS

Introduction

Logistic Regression and Generalised Linear Models

Analysis Using R

Summary

DENSITY ESTIMATION

Introduction

Density Estimation

Analysis Using R

Summary

RECURSIVE PARTITIONING

Introduction

Recursive Partitioning

Analysis Using R

Summary

SURVIVAL ANALYSIS

Introduction

Survival Analysis

Analysis Using R

Summary

ANALYSING LONGITUDINAL DATA I

Introduction

Analysing Longitudinal Data

Linear Mixed Effects models

Analysis Using R

Prediction of Random Effects

The Problem of Dropouts

Summary

ANALYSING LONGITUDINAL DATA II

Introduction

Generalised Estimating Equations

Analysis Using R

Summary

META-ANALYSIS

Introduction

Systematic Reviews and Meta-Analysis

Analysis Using R

Meta-Regression

Publication Bias

Summary

PRINCIPAL COMPONENT ANALYSIS

Introduction

Principal Component Analysis

Analysis Using R

Summary

MULTIDIMENSIONAL SCALING

Introduction

Multidimensional Scaling

Analysis Using R

Summary

CLUSTER ANALYSIS

Introduction

Cluster Analysis

Analysis Using R

Summary

BIBLIOGRAPHY

INDEX

Name: A Handbook of Statistical Analyses Using R (eBook) – Chapman and Hall/CRC

Description: By Torsten Hothorn, Brian S. Everitt. R is dynamic, to say the least. More precisely, it is organic, with new functionality and add-on packages appearing constantly. And because of its open-source nature and free availability, R is quickly becoming the software of choice for statistical...

Categories: Statistics for the Biological Sciences, Statistical Theory & Methods, Statistical Computing