Graphics for Statistics and Data Analysis with R" presents the basic principles of sound graphical design and applies these principles to engaging examplesusing the graphical functions available in R. It offers a wide array of graphical displays for the presentation of data, including modern tools for data visualization and representation. The book considers graphical displays of a single discrete variable, a single continuous variable, and then two or more of each of these. It includes displays and the R code for producing the displays for the dot chart, bar chart, pictographs, stemplot, boxplot, and variations onthe quantile-quantile plot. The author discusses nonparametric and parametricdensity estimation, diagnostic plots for the simple linear regression model, polynomial regression, and locally weighted polynomial regression for producing a smooth curve through data on a scatterplot. The last chapter illustrates visualizing multivariate data with examples using Trellis graphics. Showing howto use graphics to display or summarize data, this text provides best practice guidelines for producing and choosing among graphical displays. It also covers the most effective graphing functions in R. R code is available for download on the book's website. INDICE: INTRODUCTION The Graphical Display of Information Introduction Know the Intended Audience Principles of Effective Statistical Graphs Graphicacy Graphical Statistics A SINGLE DISCRETE VARIABLE Basic Charts for the Distribution of a Single Discrete Variable Introduction An Example from the United Nations The Dot Chart The Bar Chart The Pie Chart Advanced Charts for the Distribution of a Single Discrete Variable Introduction The Stacked Bar Chart The Pictograph Variations on the Dot and Bar Charts Frames, Grid Lines, and Order A SINGLE CONTINUOUS VARIABLE Exploratory Plots for the Distribution of a Single Continuous Variable Introduction The Dotplot The Stemplot The Boxplot The EDF Plot Diagnostic Plots for the Distribution of a Continuous Variable IntroductionThe Quantile-Quantile Plot The Probability Plot Estimation of Quartiles and Percentiles Nonparametric Density Estimation for a Single Continuous Variable Introduction The Histogram Kernel Density Estimation Spline Density Estimation Choosing a Plot for a Continuous Variable Parametric Density Estimation for a Single Continuous Variable Introduction Normal Density Estimation Transformations to Normality Pearson's Curves Gram--Charlier Series Expansion TWO VARIABLES Depicting the Distribution of Two Discrete Variables Introduction 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 TheSide-by-Side Stacked Bar Chart The Side-by-Side Pie Chart The Mosaic Chart Depicting the Distribution of One Continuous Variable and One Discrete Variable Introduction 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 Depicting the Distribution of Two Continuous Variables Introduction The Scatterplot The Sunflower Plot The Bagplot The Two-Dimensional Histogram Two-Dimensional Kernel Density Estimation STATISTICAL MODELS FOR TWO OR MORE VARIABLES Graphical Displays for Simple Linear Regression Introduction The Simple Linear Regression Model Residual Analysis Influence Analysis Graphical Displays for Polynomial Regression Introduction The Polynomial Regression Model Splines LocallyWeighted Polynomial Regression Visualizing Multivariate Data Introduction Three or More Discrete Variables One Discrete and Two or More Continuous Variables Observations of Multiple Variables The Multiple Linear Regression Model References Index Exercises appear at the end of each chapter.
- ISBN: 978-1-58488-087-5
- Editorial: CRC
- Encuadernacion: Cartoné
- Páginas: 447
- Fecha Publicación: 21/04/2010
- Nº Volúmenes: 1
- Idioma: Inglés