A Handbook of Statistical Analyses Using R

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.

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.

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 DisplaysIntroduction Initial Data AnalysisAnalysis 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 RegressionAnalysis 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 RMeta-AnalysisIntroduction 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.