A Handbook of Statistical Analyses Using R

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.

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 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