Graphics for Statistics and Data Analysis with R

 
 
Routledge Cavendish (Verlag)
  • 2. Auflage
  • |
  • erschienen am 26. September 2018
  • |
  • 610 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-429-63370-6 (ISBN)
 
Praise for the First Edition


"The main strength of this book is that it provides a unified framework of graphical tools for data analysis, especially for univariate and low-dimensional multivariate data. In addition, it is clearly written in plain language and the inclusion of R code is particularly useful to assist readers' understanding of the graphical techniques discussed in the book. ... It not only summarises graphical techniques, but it also serves as a practical reference for researchers and graduate students with an interest in data display." -Han Lin Shang, Journal of Applied Statistics


Graphics for Statistics and Data Analysis with R, Second Edition, presents the basic principles of graphical design and applies these principles to engaging examples using the graphics and lattice packages in R. It offers a wide array of modern graphical displays for data visualization and representation. Added in the second edition are coverage of the ggplot2 graphics package, material on human visualization and color rendering in R, on screen, and in print.


Features








Emphasizes the fundamentals of statistical graphics and best practice guidelines for producing and choosing among graphical displays in R







Presents technical details on topics such as: the estimation of quantiles, nonparametric and parametric density estimation; diagnostic plots for the simple linear regression model; polynomial regression, splines, and locally weighted polynomial regression for producing a smooth curve; Trellis graphics for multivariate data







Provides downloadable R code and data for figures at www.graphicsforstatistics.com





Kevin J. Keen is a Professor of Mathematics and Statistics at the University of Northern British Columbia (Prince George, Canada) and an Accredited Professional StatisticianTM by the Statistical Society of Canada and the American Statistical Association.
2. Auflage
  • Englisch
  • London
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
  • Neue Ausgabe
978-0-429-63370-6 (9780429633706)
Kevin J. Keen is a Professor of Mathematics and Statistics at the University of Northern British Columbia (Prince George, Canada) and an Accredited Professional StatisticianTM by the Statistical Society of Canada and the American Statistical Association.
List of Figures





List of Tables





Preface to the First Edition





Preface to the Second Edition





Acknowledgments





I Introduction





The Graphical Display of Information


Introduction


Learning Outcomes


Know the Intended Audience


Principles of Effective Statistical Graphs


The Layout of a Graphical Display


The Design of Graphical Displays


Graphicacy


The Grammar of Graphics


Graphical Statistics


Conclusion


Exercises





II A Single Discrete Variable





Basic Charts for the Distribution of a Single Discrete Variable


Introduction


Learning Outcomes


An Example from the United Nations


The Dot Chart


The Bar Chart


Definition


Pseudo Three-Dimensional Bar Chart


The Pie Chart


Definition


Pseudo Three-Dimensional Pie Chart


Recommendations Concerning the Pie Chart


Conclusion


Exercises





Advanced Charts for the Distribution of a Single Discrete Variable


Introduction


Learning Outcomes


The Stacked Bar Chart


Definition


The Stacked Bar Plot Versus the Bar Chart and the Pie Chart


The Pictograph


Definition


The Pictograph Versus the Dot Chart and the Bar Chart


Variations on the Dot and Bar Charts


The Bar-Whisker Chart


Dot-Whisker Chart


Frames, Grid Lines, and Order


Frame


Grid Lines


Order


Conclusion


Exercises





III A Single Continuous Variable





Exploratory Plots for the Distribution of a Single Continuous Variable


Introduction


Learning Outcomes


The Dotplot


Definition


Variations on the Dotplot


The Stemplot


Definition


The Boxplot


Definition


Variations on the Boxplot


The EDF Plot


Definition


The EDF Plot as a Diagnostic Tool


Conclusion


Exercises





Diagnostic Plots for the Distribution of a Continuous Variable


Introduction


Learning Outcomes


The Quantile-Quantile Plot


The Probability Plot


Estimation of Quartiles and Percentiles


Estimation of Quartiles


Estimation of Percentiles


Conclusion


Exercises





Nonparametric Density Estimation for a Single Continuous Variable


Introduction


Learning Outcomes


The Histogram


Definition


A Circular Variation on the Histogram: The Rose Diagram


Kernel Density Estimation


Spline Density Estimation


Choosing a Plot for a Continuous Variable


Conclusion


Exercises





Parametric Density Estimation for a Single Continuous Variable


Introduction


Learning Outcomes


Normal Density Estimation


Transformations to Normality


Pearson's Curves


Gram-Charlier Series Expansion


Conclusion


Exercises





IV Two Variables





Depicting the Distribution of Two Discrete Variables


Introduction


Learning Outcomes


The Grouped Dot Chart


The Grouped Dot-Whisker Chart


The Two-Way Dot Chart


The Multi-Valued Dot Chart


The Side-by-Side Bar Chart


The Side-by-Side Bar-Whisker Chart


The Side-by-Side Stacked Bar Chart


The Side-by-Side Pie Chart


The Mosaic Chart


Conclusion


Exercises





Depicting the Distribution of One Continuous Variable and One Discrete Variable


Introduction


Learning Outcomes


The Side-by-Side Dotplot


The Side-by-Side Boxplot


The Notched Boxplot


The Variable-Width Boxplot


The Back-to-Back Stemplot


The Side-by-Side Stemplot


The Side-by-Side Dot-Whisker Plot


The Trellis Kernel Density Estimate


Conclusion


Exercises





Depicting the Distribution of Two Continuous Variables


Introduction


Learning Outcomes


The Scatterplot


The Sunflower Plot


The Bagplot


The Two-Dimensional Histogram


Definition


The Levelplot


The Cloud Plot


Two-Dimensional Kernel Density Estimation


Definition


The Contour Plot


The Wireframe plot


Conclusion


Exercises





V Statistical Models for Two or More Variables





Simple Linear Regression: Graphical Displays


Introduction


Learning Outcomes


The Simple Linear Regression Model


Definition


The Scatterplot


The Sunflower Plot


Residual Analysis


Definition


Residual Scatterplots


Depicting the Distribution of the Residuals


Depicting the Distribution of the Semistandardized Residuals


Influence Analysis


Definition


Matrix Notation for the Simple Linear Regression Model


Depicting Standardized Residuals


Depicting the Distribution of Studentized Residuals


Depicting Leverage


Depicting DFFITS


Depicting DFBETAS


Depicting Cook's Distance


Influence Plots


Conclusion


Exercises





Polynomial Regression and Data Smoothing: Graphical Displays


Introduction


Learning Outcomes


The Polynomial Regression Model


Splines


Locally Weighted Polynomial Regression


Conclusion


Exercises





Visualizing Multivariate Data


Introduction


Learning Outcomes


Depicting Distributions of Three or More Discrete Variables


The Sinking of the Titanic


Thermometer Chart


Three-Dimensional Bar Chart


Trellis Three-Dimensional Bar Chart


Depicting Distributions of One Discrete Variable and Two or More Continuous Variables


Anderson's Iris Data


The Superposed Scatterplot


The Superposed Three-Dimensional Scatterplot


The Scatterplot Matrix


The Parallel Coordinates Plot


The Trellis Plot


Observations of Multiple Variables


OECD Healthcare Service Data


Chernoff's Faces


The Star Plot


The Rose Plot


The Multiple Linear Regression Model


Definition


Modeling Perch Mass


Residual Scatterplot Matrix


Leverage Scatterplot Matrix


Influence Plot


Partial-Regression Scatterplot Matrix


Partial-Residual Scatterplot Matrix


Summary of the Model for Perch Mass


Conclusion


Exercises





VI Appendices





Human Visualization


Introduction


Learning Outcomes


Optics


Introduction


Geometrical Optics


The Light Spectrum


Anatomy of the Human Eye


The Perception of Colour


Graphical Perception


Weber's Law


Stevens's Law


The Gestalt Laws of Organization


Kosslyn's Image Processing Model


Conclusion


Exercises





Color Rendering


Introduction


Learning Outcomes


RGB and XYZ Color Spaces


HSL and HSV Color Spaces


CIELAB and CIELUV Color Spaces


HCL Color Space


CMYK Color Space


Displaying Color in R


Saving Color Documents from R


Conclusion


Exercises





Bibliography





Index
"A leading expert wrote the book. The book is an exposition of statistical methodology that focuses on ideas and concepts and makes extensive use of graphical presentation, but readers should have some prior experience of statistical methodology. The chapters also contain many exercises with solutions and hints presented in the Appendix. The R codes are available for download on the website. The book presents data and Programmes to replicate the models developed, offers new methods that are ready to use, and explores graphical statistics in its entirety from the fundamentals of modern methods. The book is also a complete reference manual and should be considered a must-have companion for the interested advanced audience."
~International Society for Clinical Biostatistics





". . . this is a book I can recommend for consideration in a course or as a course supplement. It is generally clear and well-written, and the statistical aspects of some of these methods are explained in sufficient detail to put these in context."
~Michael Friendly, Journal of Agricultural, Biological, and Environmental Statistics

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