دانلود کتاب Analysis of categorical data with R

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Content: 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.
Abstract: "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 o er this book to help students and researchers learn how to properly analyze categorical data. Unlike other texts on similar topics, our book is a modern account using the vastly popular R software. 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. For example, we write out likelihood functions and explain how they are maximized. We describe where Wald, likelihood ratio, and score procedures come from. However, except in Appendix B, where we give a general introduction to likelihood methods, we do not frequently emphasize calculus or carry out mathematical analysis in the text. The use of calculus is mostly from a conceptual focus, rather than a mathematical one"