A Handbook of Statistical Analyses Using R, Second Edition

A Handbook of Statistical Analyses Using R, Second Edition

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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 Bio(s)

    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.

    Editorial 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

     
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