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

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 ResourceThe data sets and R software used, including the authors’ own vcd and vcdExtra packages, are available at http://cran.r-project.org.

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 TablesIntroduction Loglinear models for frequencies Fitting and testing loglinear models Equivalent logit models Zero frequencies Extending Loglinear ModelsModels 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.

Michael Friendly is a professor of psychology, founding chair of the Graduate Program in Quantitative Methods, and an associate coordinator with the Statistical Consulting Service at York University. He earned a PhD in psychology from Princeton University, specializing in psychometrics and cognitive psychology. In addition to his research interests in psychology, Professor Friendly has broad experience in data analysis, statistics, and computer applications. His main research areas are the development of graphical methods for categorical and multivariate data and the history of data visualization. He is an associate editor of the Journal of Computational and Graphical Statistics and Statistical Science. David Meyer is a professor of business informatics at the University of Applied Sciences Technikum Wien. He earned a PhD in business administration from the Vienna University of Economics and Business, with an emphasis on computational economics. Dr. Meyer has published numerous papers in various computer science and statistical journals. His research interests include R, business intelligence, data mining, and operations research.