Discrete data analysis with R : visualization and modeling techniques for categorical and count data /: visualization and modeling techniques for categorical and count data. (2016)

• Record Type: Book
• Further Information: Note: Michael Friendly, David Meyer.
• Authors: Friendly, Michael; Meyer, David, 1973-
• Contents: 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 data Fitting 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 models Exploratory 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 Tables; Introduction; Loglinear models for frequencies; Fitting and testing loglinear models; Equivalent logit models; Zero frequencies Extending Loglinear Models; Models for ordinal variables; Square tables; Three-way and higher-dimensional tables; Multivariate responses Generalized 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 models A summary and lab exercises appear at the end of each chapter.
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2016
• Extent: 1 online resource
• Subjects: 001.4226; Information visualization; Visual analytics; Data mining -- Graphic methods; R (Computer program language)
• Languages: English
• ISBNs: 9781498725866
• Related ISBNs: 9781498725859; 9781498725880
• Notes: Note: Description based on CIP data; resource not viewed.
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