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Analysis of categorical data with R

Analysis of categorical data with R (Loan 6 times)

Material type
단행본
Personal Author
Bilder, Christopher R. Loughin, Thomas M.
Title Statement
Analysis of categorical data with R / Christopher R. Bilder, University of Nebraska-Lincoln, Lincoln, Nebraska, USA, Thomas M. Loughin, Simon Fraser University, Surrey, British Columbia, Canada.
Publication, Distribution, etc
Boca Raton :   CRC Press, Taylor & Francis Group,   c2015.  
Physical Medium
xiii, 533 p. : ill. ; 26 cm.
Series Statement
Chapman & Hall/CRC texts in statistical science
ISBN
9781439855676 (hardback) 1439855676 (hardback)
요약
"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"--
Bibliography, Etc. Note
Includes bibliographical references (p. 513-523) and index.
Subject Added Entry-Topical Term
Categories (Mathematics) --Data processing. R (Computer program language).
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020 ▼a 1439855676 (hardback)
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100 1 ▼a Bilder, Christopher R.
245 1 0 ▼a Analysis of categorical data with R / ▼c Christopher R. Bilder, University of Nebraska-Lincoln, Lincoln, Nebraska, USA, Thomas M. Loughin, Simon Fraser University, Surrey, British Columbia, Canada.
260 ▼a Boca Raton : ▼b CRC Press, Taylor & Francis Group, ▼c c2015.
300 ▼a xiii, 533 p. : ▼b ill. ; ▼c 26 cm.
490 1 ▼a Chapman & Hall/CRC texts in statistical science
504 ▼a Includes bibliographical references (p. 513-523) and index.
520 ▼a "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"-- ▼c Provided by publisher.
650 0 ▼a Categories (Mathematics) ▼x Data processing.
650 0 ▼a R (Computer program language).
700 1 ▼a Loughin, Thomas M.
830 0 ▼a Chapman & Hall/CRC texts in statistical science.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 512.620285 B595a Accession No. 121235088 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

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.


Information Provided By: : Aladin

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