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Analysis of Categorical Data with R

By Christopher R. Bilder, Thomas M. Loughin

Chapman and Hall/CRC – 2014 – 547 pages

Series: Chapman & Hall/CRC Texts in Statistical Science

Purchasing Options:

  • Add to CartHardback: $89.95
    978-1-43-985567-6
    August 11th 2014

Description

Learn How to Properly Analyze Categorical Data

Analysis of Categorical Data with R presents a modern account of categorical data analysis using the popular R software. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them.

The Use of R as Both a Data Analysis Method and a Learning Tool

Requiring no prior experience with R, the text offers an introduction to the essential features and functions of R. It incorporates numerous examples from medicine, psychology, sports, ecology, and other areas, along with extensive R code and output. The authors use data simulation in R to help readers understand the underlying assumptions of a procedure and then to evaluate the procedure’s performance. They also present many graphical demonstrations of the features and properties of various analysis methods.

Web Resource

The data sets and R programs from each example are available at www.chrisbilder.com/categorical. The programs include code used to create every plot and piece of output. Many of these programs contain code to demonstrate additional features or to perform more detailed analyses than what is in the text. Designed to be used in tandem with the book, the website also uniquely provides videos of the authors teaching a course on the subject. These videos include live, in-class recordings, which instructors may find useful in a blended or flipped classroom setting. The videos are also suitable as a substitute for a short course.

Reviews

"… a valuable asset to any person who wants to analyze categorical data.

Bilder and Loughin demystify categorical data analysis using a simple approach, with enough statistical theory to allow the reader to understand the underlying assumptions of the analyses involved, but with minimal, unintimidating mathematical symbols, and equations. The authors have managed to explain the statistics involved in categorical data analysis in unadorned semantics and accompanied them with corresponding R codes … . This is a major plus for this book.

Overall, the book is well written: It contains easy-to-follow R codes, footnote explanations of material that could not be explained within the text, and plenty of exercises at the end of each chapter. … Excellent videos of Bilder teaching the material in class, full R codes, and corresponding data, each arranged by chapter, are available on a website. These resources make it easy for readers to acquire a deeper understanding of categorical data analysis. …

This book is a must-have tool for any biostatistician analyzing categorical data in R. It could very well be used as a text in intermediate-to-advanced applied courses in practical analysis of categorical data."

Biometrical Journal, 2014

"Bilder and Loughin have worked as a dynamic duo for a number of years, and they clearly are blending their knowledge, talents, experience, and teamwork to create this valuable book. Analyzing categorical data correctly and in-depth is not as simple as it appears in many courses and textbooks. As a result, many people can get the wrong idea about what could and should be done with categorical data, and hence their results can be inconclusive or incorrect. This book gives users the full scoop when it comes to analyzing categorical data of all types, and it does so in an easy-to-understand way, giving confidence to the reader to go ahead and apply the ideas in practice. The use of R for analyzing data is becoming a worldwide phenomenon and a staple for data analysts on every level. As its popularity grows, it becomes critical for beginners to become comfortable with understanding and using R to analyze their data. Through the special attention paid to teaching the basics of R, as well as providing step-by-step particulars in using R in each separate analysis, Bilder and Loughin help establish and promote a group of confident, comfortable users of this software that can seem a mystery to many. I highly and happily recommend this book to anyone who plans to analyze categorical data in their careers—which includes most all of us!"

—Deborah J. Rumsey, PhD, Auxiliary Professor and Statistics Education Specialist, Department of Statistics, The Ohio State University

Contents

Analyzing a Binary Response, Part 1: Introduction

One binary variable

Two binary variables

Analyzing a Binary Response, Part 2: Regression Models

Linear regression models

Logistic regression models

Generalized linear models

Analyzing a Multicategory Response

Multinomial probability distribution

I x J contingency tables and inference procedures

Nominal response regression models

Ordinal response regression models

Additional regression models

Analyzing a Count Response

Poisson model for count data

Poisson regression models for count responses

Poisson rate regression

Zero inflation

Model Selection and Evaluation

Variable selection

Tools to assess model fit

Overdispersion

Examples

Additional Topics

Binary responses and testing error

Exact inference

Categorical data analysis in complex survey designs

"Choose all that apply" data

Mixed models and estimating equations for correlated data

Bayesian methods for categorical data

Appendix A: An Introduction to R

Appendix B: Likelihood Methods

Bibliography

Index

Exercises appear at the end of each chapter.

Name: Analysis of Categorical Data with R (Hardback)Chapman and Hall/CRC 
Description: By Christopher R. Bilder, Thomas M. Loughin. Learn How to Properly Analyze Categorical DataAnalysis of Categorical Data with R presents a modern account of categorical data analysis using the popular R software. It covers recent techniques of model building and assessment for binary, multicategory,...
Categories: Statistical Theory & Methods, Statistical Computing, Statistics for the Biological Sciences