"A Chapman & Hall book."

Includes bibliographical references and index.

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 dataFitting 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 modelsExploratory 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 responsesGeneralized 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 modelsA summary and lab exercises appear at the end of each chapter.