Interactive and Dynamic Graphics for Data Analysis

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Format: Paperback
Pub. Date: 2007-10-26
Publisher(s): Springer Verlag
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Summary

This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models. All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples. The book may be used as a text in a class on statistical graphics or exploratory data analysis, for example, or as a guide for the independent learner. Each chapter ends with a set of exercises. The authors are both Fellows of the American Statistical Association, past chairs of the Section on Statistical Graphics, and co-authors of the GGobi software. Dianne Cook is Professor of Statistics at Iowa State University. Deborah Swayne is a member of the Statistics Research Department at AT&T Labs.

Author Biography

Dianne Cook is Professor of Statistics at Iowa State University.

Table of Contents

Prefacep. V
Technical Notesp. XIII
List of Figuresp. XV
Introductionp. 1
Data visualization: beyond the third dimensionp. 1
Statistical data visualization: goals and historyp. 3
Getting down to datap. 4
Getting real: process and caveatsp. 8
Interactive investigationp. 15
The Toolboxp. 17
Introductionp. 17
Plot typesp. 19
Univariate plotsp. 19
Bivariate plotsp. 21
Multivariate plotsp. 24
Plot arrangementp. 34
Plot manipulation and enhancementp. 35
Brushingp. 35
Identificationp. 41
Scalingp. 41
Subset selectionp. 42
Line segmentsp. 43
Interactive drawingp. 43
Dragging pointsp. 43
Tools available elsewherep. 44
Recapp. 45
Exercisesp. 45
Missing Valuesp. 47
Backgroundp. 48
Exploring missingnessp. 49
Shadow matrixp. 49
Getting started: missings in the "margins"p. 52
A limitationp. 53
Tracking missings using the shadow matrixp. 55
Imputationp. 55
Mean valuesp. 56
Random valuesp. 56
Multiple imputationp. 58
Recapp. 61
Exercisesp. 62
Supervised Classificationp. 63
Backgroundp. 64
Classical multivariate statisticsp. 65
Data miningp. 66
Studying the fitp. 69
Purely graphics: getting a picture of the class structurep. 70
Overview of Italian Olive Oilsp. 70
Building classifiers to predict regionp. 71
Separating the oils by area within each regionp. 73
Taking stockp. 77
Numerical methodsp. 77
Linear discriminant analysisp. 77
Treesp. 81
Random forestsp. 83
Neural networksp. 88
Support vector machinep. 92
Examining boundariesp. 97
Recapp. 99
Exercisesp. 99
Cluster Analysisp. 103
Backgroundp. 105
Purely graphicsp. 107
Numerical methodsp. 111
Hierarchical algorithmsp. 111
Model-based clusteringp. 113
Self-organizing mapsp. 119
Comparing methodsp. 122
Characterizing clustersp. 125
Recapp. 126
Exercisesp. 127
Miscellaneous Topicsp. 129
Inferencep. 129
Longitudinal datap. 134
Network datap. 139
Multidimensional scalingp. 145
Exercisesp. 151
Datasetsp. 153
Tipsp. 153
Australian Crabsp. 154
Italian Olive Oilsp. 155
Flea Beetlesp. 157
PRIM7p. 157
Tropical Atmosphere-Ocean Array (TAO)p. 159
Primary Biliary Cirrhosis (PBC)p. 161
Spamp. 162
Wagesp. 164
Rat Gene Expressionp. 166
Arabidopsis Gene Expressionp. 168
Musicp. 171
Cluster Challengep. 172
Adjacent Transposition Graphp. 172
Florentine Familiesp. 173
Morse Code Confusion Ratesp. 174
Personal Social Networkp. 175
Referencesp. 177
Indexp. 185
Table of Contents provided by Ingram. All Rights Reserved.

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