R for Statistics

About this book

Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples.

Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R. Focusing on the R software, the first section covers: Basic elements of the R software and data processing Clear, concise visualization of results, using simple and complex graphs Programming basics: pre-defined and user-created functions The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including: Regression methods Analyses of variance and covariance Classification methods Exploratory multivariate analysis Clustering methods Hypothesis tests After a short presentation of the method, the book explicitly details the R command lines and gives commented results.

Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist. Datasets and all the results described in this book are available on the book's webpage at http://www.agrocampus-ouest.fr/math/RforStat

Contents

An Overview of R
Main Concepts
Installing R
Work Session
Help
R Objects
Functions
Packages
Exercises

Preparing Data
Reading Data from File
Exporting Results
Manipulating Variables
Manipulating Individuals
Concatenating Data Tables
Cross-Tabulation
Exercises

R Graphics
Conventional Graphical Functions
Graphical Functions with lattice
Exercises

Making Programs with R
Control Flows
Predefined Functions
Creating a Function
Exercises

Statistical Methods
Introduction to the Statistical Methods

A Quick Start with R
Installing R
Opening and Closing R
The Command Prompt
Attribution, Objects, and Function
Selection
Other
Rcmdr Package
Importing (or Inputting) Data
Graphs
Statistical Analysis

Hypothesis Test
Confidence Intervals for a Mean
Chi-Square Test of Independence
Comparison of Two Means
Testing Conformity of a Proportion
Comparing Several Proportions
The Power of a Test

Regression
Simple Linear Regression
Multiple Linear Regression
Partial Least Squares (PLS) Regression

Analysis of Variance and Covariance
One-Way Analysis of Variance
Multi-Way Analysis of Variance with Interaction
Analysis of Covariance

Classification
Linear Discriminant Analysis
Logistic Regression
Decision Tree

Exploratory Multivariate Analysis
Principal Component Analysis
Correspondence Analysis
Multiple Correspondence Analysis

Clustering
Ascending Hierarchical Clustering
The k-Means Method

Appendix
The Most Useful Functions
Writing a Formula for the Models
The Rcmdr Package
The FactoMineR Package
Answers to the Exercises

Customer Reviews