Discrete data analysis with R: visualization and modeling techniques for categorical and count data
Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical methods for exploring data, spott...
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press Taylor & Francis Group
[2016]
|
| Series: | Texts in Statistical Science Series
120 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical methods for exploring data, spotting unusual features, visualizing fitted models, and presenting results.The book is designed for advanced undergraduate and graduate students in the social and health sciences, epidemiology, economics, business, statistics, and biostatistics as well as researchers, methodologists, and consultants who can use the methods with their own data and analyses. It can be used for self-study, as the primary textbook for a course in categorical data analysis, or as a supplementary text for such a course. |
| Item Description: | The data sets and R software used, including the authors’ own vcd and vcdExtra packages, are available at http://cran.r-project.org |
| Physical Description: | xvii, 544 Seiten Illustrationen, Diagramme |
| ISBN: | 9781498725835 |
Staff View
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| 245 | 1 | 0 | |a Discrete data analysis with R |b visualization and modeling techniques for categorical and count data |c Michael Friendly, York University Toronto, Canada ; David Meyer, UAS Technikum Wien, vienna, Austria ; with contributions by Achim Zeileis, University of Innsbruck, Innsbruck, Austria |
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press Taylor & Francis Group |c [2016] | |
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| 490 | 1 | |a Texts in Statistical Science Series |v 120 | |
| 500 | |a The data sets and R software used, including the authors’ own vcd and vcdExtra packages, are available at http://cran.r-project.org | ||
| 505 | 8 | |a Getting Started: Introduction. Working with Categorical Data. Fitting and Graphing Discrete Distributions. Exploratory and Hypothesis-Testing Methods: Two-Way Contingency Tables. Mosaic Displays for n-Way Tables. Correspondence Analysis. Model-Building Methods: Logistic Regression Models. Models for Polytomous Responses. Loglinear and Logit Models for Contingency Tables. Extending Loglinear Models. Generalized Linear Models for Count Data. | |
| 505 | 8 | |a Includes bibliographical references and index | |
| 520 | 3 | |a Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical methods for exploring data, spotting unusual features, visualizing fitted models, and presenting results.The book is designed for advanced undergraduate and graduate students in the social and health sciences, epidemiology, economics, business, statistics, and biostatistics as well as researchers, methodologists, and consultants who can use the methods with their own data and analyses. It can be used for self-study, as the primary textbook for a course in categorical data analysis, or as a supplementary text for such a course. | |
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| adam_text | Contents
Preface xiii
I Getting Started 1
1 Introduction 3
1.1 Data visualization and categorical data: Overview ...................... 3
1.2What is categorical data?..................................................... 4
1.2.1 Case form vs. frequency form......................................... 5
1.2.2 Frequency data vs. count data ....................................... 6
1.2.3 Univariate, bivariate, and multivariate data......................... 7
1.2.4 Explanatory vs. response variables................................... 7
1.3 Strategies for categorical data analysis ............................... 8
1.3.1 Hypothesis testing approaches........................................ 8
1.3.2 Model building approaches........................................... 10
1.4 Graphical methods for categorical data .................................... 13
1.4.1 Goals and design principles for visual data display................. 13
1.4.2 Categorical data require different graphical methods................ 17
1.4.3 Effect ordering and rendering for data display...................... 18
1.4.4 Interactive and dynamic graphics.................................... 21
1.4.5 Visualization = Graphing + Fitting + Graphing....................... 22
1.4.6 Data plots, model plots, and data+model plots....................... 25
1.4.7 The 80-20 rule...................................................... 26
1.5 Chapter summary ........................................................... 28
1.6 Lab exercises ............................................................. 29
2 Working with Categorical Data 31
2.1 Working with R data: vectors, matrices, arrays, and data frames ........ 32
2.1.1 Vectors............................................................. 32
2.1.2 Matrices............................................................ 33
2.1.3 Arrays.............................................................. 35
2.1.4 Data frames......................................................... 37
2.2 Forms of categorical data: case form, frequency form, and table form ... 39
2.2.1 Case form........................................................... 39
vii
V1U
Contents
2.2.2 Frequency form......................................................... 40
2.2.3 Table form............................................................. 41
2.3 Ordered factors and reordered tables ....................................... 43
2.4 Generating tables: table and xtabs.......................................... 44
2.4.1 tableQ................................................................. 44
2.4.2 xtabs()................................................................ 46
2.5 Printing tables: structable and ftable...................................... 47
2.5.1 Text output ........................................................... 47
2.6 Subsetting data .............................................................. 48
2.6.1 Subsetting tables...................................................... 48
2.6.2 Subsetting structables................................................. 49
2.6.3 Subsetting data frames................................................. 50
2.7 Collapsing tables ............................................................ 51
2.7.1 Collapsing over table factors ......................................... 51
2.7.2 Collapsing table levels................................................ 53
2.8 Converting among frequency tables and data frames .......................... 53
2.8.1 Table form to frequency form........................................... 54
2.8.2 Case form to table form................................................ 55
2.8.3 Table form to case form................................................ 55
2.8.4 Publishing tables to IfT^X or HTML................................... 56
2.9 A complex example: TV viewing data*......................................... 58
2.9.1 Creating data frames and arrays........................................ 58
2.9.2 Subsetting and collapsing.............................................. 60
2.10 Lab exercises ............................................................... 60
3 Fitting and Graphing Discrete Distributions 65
3.1 Introduction to discrete distributions ..................................... 66
3.1.1 Binomial data.......................................................... 66
3.1.2 Poisson data........................................................... 69
3.1.3 Type-token distributions............................................... 72
3.2 Characteristics of discrete distributions .................................. 73
3.2.1 The binomial distribution.............................................. 74
3.2.2 The Poisson distribution............................................... 76
3.2.3 The negative binomial distribution .................................... 82
3.2.4 The geometric distribution............................................. 85
3.2.5 The logarithmic series distribution ................................... 86
3.2.6 Power series family ................................................... 86
3.3 Fitting discrete distributions................................................ 87
3.3.1 R tools for discrete distributions..................................... 89
3.3.2 Plots of observed and fitted frequencies............................... 92
3.4 Diagnosing discrete distributions: Ord plots................................ 95
3.5 Poissonness plots and generalized distribution plots........................ 99
3.5.1 Features of the Poissonness plot...................................... 100
3.5.2 Plot construction..................................................... 100
3.5.3 The distplot function................................................. 101
3.5.4 Plots for other distributions......................................... 102
3.6 Fitting discrete distributions as generalized linear models* ............... 104
3.6.1 Covariates, overdispersion, and excess zeros......................... 107
3.7 Chapter summary .............................................................. 109
Contents ix
3.8 Lab exercises .......................................................... 109
II Exploratory and Hypothesis-Testing Methods 113
4 Two-Way Contingency Tables 115
4.1 Introduction............................................................. N5
4.2 Tests of association for two-way tables ................................ 119
4.2.1 Notation and terminology.......................................... 119
4.2.2 2 by 2 tables: Odds and odds ratios.............................. 121
4.2.3 Larger tables: Overall analysis.................................. 124
4.2.4 Tests for ordinal variables....................................... 125
4.2.5 Sample CMH profiles............................................... 126
4.3 Stratified analysis .................................................... 127
4.3.1 Computing strata-wise statistics ................................. 128
4.3.2 Assessing homogeneity of association.............................. 129
4.4 Fourfold display for 2 x 2 tables....................................... 130
4.4.1 Confidence rings for odds ratio.................................. 133
4.4.2 Stratified analysis for 2 x 2 x ktables ......................... 133
4.5 Sieve diagrams ......................................................... 138
4.5.1 Two-way tables.................................................... 138
4.5.2 Larger tables: The strucplot framework .......................... 141
4.6 Association plots....................................................... 145
4.7 Observer agreement ..................................................... 146
4.7.1 Measuring agreement............................................... 148
4.7.2 Observer agreement chart ......................................... 150
4.7.3 Observer bias in agreement........................................ 152
4.8 Trilinear plots......................................................... 153
4.9 Chapter summary ........................................................ 157
4.10 Lab exercises .......................................................... 158
5 Mosaic Displays for n-Way Tables 161
5.1 Introduction............................................................ 161
5.2 Two-way tables.......................................................... 162
5.2.1 Shading levels.................................................... 166
5.2.2 Interpretation and reordering..................................... 166
5.3 The strucplot framework ................................................ 167
5.3.1 Components overview............................................... 167
5.3.2 Shading schemes................................................... 169
5.4 Three-way and larger tables ............................................ 176
5.4.1 A primer on logllnear models...................................... 177
5.4.2 Fitting models.................................................... 179
5.5 Model and plot collections ............................................. 183
5.5.1 Sequential plots and models ...................................... 184
5.5.2 Causal models..................................................... 186
5.5.3 Partial association............................................... 188
5.6 Mosaic matrices for categorical data.................................... 197
5.6.1 Mosaic matrices for pairwise associations......................... 197
5.6.2 Generalized mosaic matrices and pairs plots....................... 201
5.7 3D mosaics.............................................................. 203
5.8 Visualizing the structure of loglinear models .......................... 205
5.8.1 Mutual independence............................................... 206
x Contents
5.8.2 Joint independence .............................................. 208
5.9 Related visualization methods ........................................... 209
5.9.1 Doubledecker plots................................................ 209
5.9.2 Generalized odds ratios*.......................................... 211
5.10 Chapter summary ......................................................... 215
5.11 Lab exercises ........................................................... 216
6 Correspondence Analysis 221
6.1 Introduction........................................................... 221
6.2 Simple correspondence analysis......................................... 222
6.2.1 Notation and terminology........................................ 222
6.2.2 Geometric and statistical properties ............................. 224
6.2.3 R software for correspondence analysis............................ 224
6.2.4 Correspondence analysis and mosaic displays....................... 231
6.3 Multi-way tables: Stacking and other tricks ........................... 232
6.3.1 Interactive coding in R .......................................... 233
6.3.2 Marginal tables and supplementary variables....................... 238
6.4 Multiple correspondence analysis ........................................ 240
6.4.1 Bivariate MCA..................................................... 240
6.4.2 The Burt matrix................................................... 243
6.4.3 Multivariate MCA.................................................. 243
6.5 Biplots for contingency tables........................................... 248
6.5.1 CA bilinear biplots............................................... 248
6.5.2 Biadditive biplots................................................ 252
6.6 Chapter summary ......................................................... 254
6.7 Lab exercises ........................................................... 254
III Model-Building Methods 259
7 Logistic Regression Models 261
7.1 Introduction............................................................. 261
7.2 The logistic regression model............................................ 263
7.2.1 Fitting a logistic regression model .............................. 265
7.2.2 Model tests for simple logistic regression ....................... 267
7.2.3 Plotting a binary response........................................ 268
7.2.4 Grouped binomial data ............................................ 270
7.3 Multiple logistic regression models...................................... 272
7.3.1 Conditional plots................................................. 275
7.3.2 Full-model plots ................................................. 276
7.3.3 Effect plots...................................................... 278
7.4 Case studies............................................................ 281
7.4.1 Simple models: Group comparisons and effect plots................. 282
7.4.2 More complex models: Model selection and visualization ........... 294
7.5 Influence and diagnostic plots .......................................... 303
7.5.1 Residuals and leverage............................................ 303
7.5.2 Influence diagnostics ............................................ 304
7.5.3 Other diagnostic plots* .......................................... 312
7.6 Chapter summary ......................................................... 319
7.7 Lab exercises ........................................................... 320
Contents
XI
8 Models for Polytomous Responses 323
8.1 Ordinal response ........................................................ 324
8.1.1 Latent variable interpretation.................................... 325
8.1.2 Fitting the proportional odds model.............................. 326
8.1.3 Testing the proportional odds assumption......................... 327
8.1.4 Graphical assessment of proportional odds......................... 329
8.1.5 Visualizing results for the proportional odds model.............. 331
8.2 Nested dichotomies ...................................................... 335
8.3 Generalized logit model.................................................. 341
8.4 Chapter summary ......................................................... 346
8.5 Lab exercises ........................................................... 346
9 Loglinear and Logit Models for Contingency Tables 349
9.1 Introduction............................................................. 349
9.2 Loglinear models for frequencies......................................... 350
9.2.1 Loglinear models as ANOVA models for frequencies................. 350
9.2.2 Loglinear models for three-way tables............................ 352
9.2.3 Loglinear models as GLMs for frequencies ........................ 352
9.3 Fitting and testing loglinear models .................................... 353
9.3.1 Model fitting functions............................................ 353
9.3.2 Goodness-of-fit tests.............................................. 354
9.3.3 Residuals for loglinear models .................................... 356
9.3.4 Using loglm()...................................................... 357
9.3.5 Using glm()........................................................ 359
9.4 Equivalent logit models ................................................. 363
9.5 Zero frequencies......................................................... 368
9.6 Chapter summary ......................................................... 372
9.7 Lab exercises ........................................................... 372
10 Extending Loglinear Models 375
10.1 Models for ordinal variables............................................. 376
10.1.1 Loglinear models for ordinal variables............................ 376
10.1.2 Visualizing model structure....................................... 381
10.1.3 Log-multiplicative (RC) models.................................... 382
10.2 Square tables ........................................................... 389
10.2.1 Quasi-independence, symmetry, quasi-symmetry, and topological mod-
els ...................................................................... 389
10.2.2 Ordinal square tables............................................. 396
10.3 Three-way and higher-dimensional tables.................................. 400
10.4 Multivariate responses*.................................................. 403
10.4.1 Bivariate, binary response models................................. 405
10.4.2 More complex models............................................... 415
10.5 Chapter summary ......................................................... 425
10.6 Lab exercises ........................................................... 426
11 Generalized Linear Models for Count Data 429
11.1 Components of generalized linear models.................................. 430
11.1.1 Variance functions................................................ 431
11.1.2 Hypothesis tests for coefficients ............................... 432
11.1.3 Goodness-of-fit tests............................................. 433
11.1.4 Comparing non-nested models....................................... 434
*· Contents
11.2 GLMs for count data.................................................... 435
11.3 Models for overdispersed count data.................................... 444
11.3.1 The quasi-Poisson model........................................... 445
11.3.2 The negative-binomial model....................................... 446
11.3.3 Visualizing the mean-variance relation............................ 447
11.3.4 Testing overdispersion............................................ 449
11.3.5 Visualizing goodness-of-fit....................................... 450
11.4 Models for excess zero counts............................................ 451
11.4.1 Zero-inflated models.............................................. 452
11.4.2 Hurdle models..................................................... 454
11.4.3 Visualizing zero counts........................................... 454
11.5 Case studies............................................................. 456
11.5.1 Cod parasites..................................................... 456
11.5.2 Demand for medical care by the elderly............................ 468
11.6 Diagnostic plots for model checking ................................... 480
11.6.1 Diagnostic measures and residuals for GLMs...................... 480
11.6.2 Quantile-quantile and half-normal plots........................... 485
11.7 Multivariate response GLM models*........................................ 489
11.7.1 Analyzing correlations: HE plots.................................. 491
11.7.2 Analyzing associations: Odds ratios and fourfold plots............ 492
11.8 Chapter summary ......................................................... 500
11.9 Lab exercises ........................................................... 501
References 505
Author Index 525
Example Index 52®
Subject Index
|
| any_adam_object | 1 |
| author | Friendly, Michael 1945- Meyer, David 1973- |
| author_GND | (DE-588)140922571 (DE-588)1089062125 |
| author_facet | Friendly, Michael 1945- Meyer, David 1973- |
| author_role | aut aut |
| author_sort | Friendly, Michael 1945- |
| author_variant | m f mf d m dm |
| building | Verbundindex |
| bvnumber | BV043309212 |
| classification_rvk | SK 840 ST 601 |
| contents | Getting Started: Introduction. Working with Categorical Data. Fitting and Graphing Discrete Distributions. Exploratory and Hypothesis-Testing Methods: Two-Way Contingency Tables. Mosaic Displays for n-Way Tables. Correspondence Analysis. Model-Building Methods: Logistic Regression Models. Models for Polytomous Responses. Loglinear and Logit Models for Contingency Tables. Extending Loglinear Models. Generalized Linear Models for Count Data. Includes bibliographical references and index |
| ctrlnum | (OCoLC)933729601 (DE-599)BVBBV043309212 |
| dewey-full | 519.50285/5133 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.50285/5133 |
| dewey-search | 519.50285/5133 |
| dewey-sort | 3519.50285 45133 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
| format | Book |
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| id | DE-604.BV043309212 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:22:46Z |
| institution | BVB |
| isbn | 9781498725835 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028729904 |
| oclc_num | 933729601 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-M347 DE-739 DE-188 DE-945 DE-355 DE-BY-UBR DE-384 |
| owner_facet | DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-M347 DE-739 DE-188 DE-945 DE-355 DE-BY-UBR DE-384 |
| physical | xvii, 544 Seiten Illustrationen, Diagramme |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | CRC Press Taylor & Francis Group |
| record_format | marc |
| series | Texts in Statistical Science Series |
| series2 | Texts in Statistical Science Series |
| spelling | Friendly, Michael 1945- Verfasser (DE-588)140922571 aut Discrete data analysis with R visualization and modeling techniques for categorical and count data Michael Friendly, York University Toronto, Canada ; David Meyer, UAS Technikum Wien, vienna, Austria ; with contributions by Achim Zeileis, University of Innsbruck, Innsbruck, Austria Boca Raton ; London ; New York CRC Press Taylor & Francis Group [2016] xvii, 544 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Texts in Statistical Science Series 120 The data sets and R software used, including the authors’ own vcd and vcdExtra packages, are available at http://cran.r-project.org Getting Started: Introduction. Working with Categorical Data. Fitting and Graphing Discrete Distributions. Exploratory and Hypothesis-Testing Methods: Two-Way Contingency Tables. Mosaic Displays for n-Way Tables. Correspondence Analysis. Model-Building Methods: Logistic Regression Models. Models for Polytomous Responses. Loglinear and Logit Models for Contingency Tables. Extending Loglinear Models. Generalized Linear Models for Count Data. Includes bibliographical references and index Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical methods for exploring data, spotting unusual features, visualizing fitted models, and presenting results.The book is designed for advanced undergraduate and graduate students in the social and health sciences, epidemiology, economics, business, statistics, and biostatistics as well as researchers, methodologists, and consultants who can use the methods with their own data and analyses. It can be used for self-study, as the primary textbook for a course in categorical data analysis, or as a supplementary text for such a course. Datenanalyse (DE-588)4123037-1 gnd rswk-swf Visualisierung (DE-588)4188417-6 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Datenanalyse (DE-588)4123037-1 s Visualisierung (DE-588)4188417-6 s R Programm (DE-588)4705956-4 s DE-604 Meyer, David 1973- Verfasser (DE-588)1089062125 aut Texts in Statistical Science Series 120 (DE-604)BV022819715 120 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028729904&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Friendly, Michael 1945- Meyer, David 1973- Discrete data analysis with R visualization and modeling techniques for categorical and count data Texts in Statistical Science Series Getting Started: Introduction. Working with Categorical Data. Fitting and Graphing Discrete Distributions. Exploratory and Hypothesis-Testing Methods: Two-Way Contingency Tables. Mosaic Displays for n-Way Tables. Correspondence Analysis. Model-Building Methods: Logistic Regression Models. Models for Polytomous Responses. Loglinear and Logit Models for Contingency Tables. Extending Loglinear Models. Generalized Linear Models for Count Data. Includes bibliographical references and index Datenanalyse (DE-588)4123037-1 gnd Visualisierung (DE-588)4188417-6 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4188417-6 (DE-588)4705956-4 |
| title | Discrete data analysis with R visualization and modeling techniques for categorical and count data |
| title_auth | Discrete data analysis with R visualization and modeling techniques for categorical and count data |
| title_exact_search | Discrete data analysis with R visualization and modeling techniques for categorical and count data |
| title_full | Discrete data analysis with R visualization and modeling techniques for categorical and count data Michael Friendly, York University Toronto, Canada ; David Meyer, UAS Technikum Wien, vienna, Austria ; with contributions by Achim Zeileis, University of Innsbruck, Innsbruck, Austria |
| title_fullStr | Discrete data analysis with R visualization and modeling techniques for categorical and count data Michael Friendly, York University Toronto, Canada ; David Meyer, UAS Technikum Wien, vienna, Austria ; with contributions by Achim Zeileis, University of Innsbruck, Innsbruck, Austria |
| title_full_unstemmed | Discrete data analysis with R visualization and modeling techniques for categorical and count data Michael Friendly, York University Toronto, Canada ; David Meyer, UAS Technikum Wien, vienna, Austria ; with contributions by Achim Zeileis, University of Innsbruck, Innsbruck, Austria |
| title_short | Discrete data analysis with R |
| title_sort | discrete data analysis with r visualization and modeling techniques for categorical and count data |
| title_sub | visualization and modeling techniques for categorical and count data |
| topic | Datenanalyse (DE-588)4123037-1 gnd Visualisierung (DE-588)4188417-6 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Datenanalyse Visualisierung R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028729904&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV022819715 |
| work_keys_str_mv | AT friendlymichael discretedataanalysiswithrvisualizationandmodelingtechniquesforcategoricalandcountdata AT meyerdavid discretedataanalysiswithrvisualizationandmodelingtechniquesforcategoricalandcountdata |