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.
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.
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