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An Applied Treatment of Modern Graphical Methods for Analyzing Categorical Data


Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical methods for exploring data, spotting unusual features, visualizing fitted models, and presenting results.





The book is designed for advanced undergraduate and graduate students in the social and health sciences, epidemiology, economics, business, statistics, and biostatistics as well as researchers, methodologists, and consultants who can use the methods with their own data and analyses. Along with describing the necessary statistical theory, the authors illustrate the practical application of the techniques to a large number of substantive problems, including how to organize data, conduct an analysis, produce informative graphs, and evaluate what the graphs reveal about the data.





The first part of the book contains introductory material on graphical methods for discrete data, basic R skills, and methods for fitting and visualizing one-way discrete distributions. The second part focuses on simple, traditional nonparametric tests and exploratory methods for visualizing patterns of association in two-way and larger frequency tables. The final part of the text discusses model-based methods for the analysis of discrete data.


Web Resource
The data sets and R software used, including the authors' own vcd and vcdExtra packages, are available at http://cran.r-project.org. "This is an excellent book, nearly encyclopedic in its coverage. I personally find it very useful and expect that many other readers will as well. The book can certainly serve as a reference. It could also serve as a supplementary text in a course on categorical data analysis that uses R for computation or-because so much statistical detail is provided-even as the main text for a course on the topic that emphasizes graphical methods."
-John Fox, McMaster University


"For many years, Prof. Friendly has been the most effective promoter in Statistics of graphical methods for categorical data. We owe thanks to Friendly and Meyer for promoting graphical methods and showing how easy it is to implement them in R. This impressive book is a very worthy addition to the library of anyone who spends much time analyzing categorical data." (Alan Agresti, Biometrics)

Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data

Getting Started

Introduction

Data visualization and categorical data: Overview

What is categorical data?

Strategies for categorical data analysis

Graphical methods for categorical data











Working with Categorical Data

Working with R data: vectors, matrices, arrays, and data frames

Forms of categorical data: case form, frequency form, and table form

Ordered factors and reordered tables

Generating tables: table and xtabs

Printing tables: structable and ftable

Subsetting data

Collapsing tables

Converting among frequency tables and data frames

A complex example: TV viewing data











Fitting and Graphing Discrete Distributions

Introduction to discrete distributions

Characteristics of discrete distributions

Fitting discrete distributions

Diagnosing discrete distributions: Ord plots

Poissonness plots and generalized distribution plots

Fitting discrete distributions as generalized linear models











Exploratory and Hypothesis-Testing Methods

Two-Way Contingency Tables

Introduction

Tests of association for two-way tables

Stratified analysis

Fourfold display for 2 x 2 tables

Sieve diagrams

Association plots

Observer agreement

Trilinear plots











Mosaic Displays for n-Way Tables

Introduction

Two-way tables

The strucplot framework

Three-way and larger tables

Model and plot collections

Mosaic matrices for categorical data

3D mosaics

Visualizing the structure of loglinear models

Related visualization methods











Correspondence Analysis

Introduction

Simple correspondence analysis

Multi-way tables: Stacking and other tricks

Multiple correspondence analysis

Biplots for contingency tables











Model-Building Methods

Logistic Regression Models

Introduction

The logistic regression model

Multiple logistic regression models

Case studies

Influence and diagnostic plots











Models for Polytomous Responses

Ordinal response

Nested dichotomies

Generalized logit model











Loglinear and Logit Models for Contingency Tables

Introduction

Loglinear models for frequencies

Fitting and testing loglinear models

Equivalent logit models

Zero frequencies











Extending Loglinear Models

Models for ordinal variables

Square tables

Three-way and higher-dimensional tables

Multivariate responses











Generalized Linear Models for Count Data

Components of generalized linear models

GLMs for count data

Models for overdispersed count data

Models for excess zero counts

Case studies

Diagnostic plots for model checking

Multivariate response GLM models











A summary and lab exercises appear at the end of each chapter.