Analysis of categorical data with R. Christopher R. Bilder and Thomas M. Loughin

1. Analyzing a binary response, part 1: introduction --
2. Analyzing a binary response, part 2: regression models --
3. Analyzing a multicategory response --
4. Analyzing a count response --
5. Model selection and evaluation --
6. Additional topics --
A. An introduction to R --
B. Likelihood methods.

We live in a categorical world! From a positive or negative disease diagnosis to choosing all items that apply in a survey, outcomes are frequently organized into categories so that people can more easily make sense of them. However, analyzing data from categorical responses requires specialized techniques beyond those learned in a first or second course in Statistics. We use R not only as a data analysis method but also as a learning tool. For example, we use data simulation to help readers understand the underlying assumptions of a procedure and then to evaluate that procedure's performance. We also provide numerous graphical demonstrations of the features and properties of various analysis methods. The focus of this book is on the analysis of data, rather than on the mathematical development of methods. We o er numerous examples from a wide rage of disciplines medicine, psychology, sports, ecology, and others and provide extensive R code and output as we work through the examples. We give detailed advice and guidelines regarding which procedures to use and why to use them. While we treat likelihood methods as a tool, they are not used blindly.