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Graphics for Statistics and Data Analysis with R

Medium: Buch
ISBN: 978-1-58488-087-5
Verlag: Taylor & Francis
Erscheinungstermin: 05.05.2010
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Graphics for Statistics and Data Analysis with R presents the basic principles of sound graphical design and applies these principles to engaging examples using 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 on the quantile-quantile plot. The author discusses nonparametric and parametric density 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 how to 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.

Produkteigenschaften

  • Artikelnummer: 9781584880875
  • Medium: Buch
  • ISBN: 978-1-58488-087-5
  • Verlag: Taylor & Francis
  • Erscheinungstermin: 05.05.2010
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2010
  • Serie: Chapman & Hall/CRC Texts in Statistical Science
  • Produktform: Gebunden
  • Gewicht: 816 g
  • Seiten: 489
  • Format (B x H): 156 x 235 mm
  • Ausgabetyp: Kein, Unbekannt
  • Nachauflage: 978-1-4987-7983-8

Themen

Autoren/Hrsg.

Autoren

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
Introduction

The 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

The Side-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

Locally Weighted 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.