R for Statistics: 1st Edition (Paperback) book cover

R for Statistics

1st Edition

By Pierre-Andre Cornillon, Arnaud Guyader, Francois Husson, Nicolas Jegou, Julie Josse, Maela Kloareg, Eric Matzner-Lober, Laurent Rouvière

Chapman and Hall/CRC

320 pages | 89 B/W Illus.

Purchasing Options:$ = USD
Paperback: 9781439881453
pub: 2012-03-21
SAVE ~$11.24
$74.95
$63.71
x
Hardback: 9781138469341
pub: 2017-09-28
SAVE ~$30.75
$205.00
$174.25
x
eBook (VitalSource) : 9781466511033
pub: 2012-04-04
from $36.48


FREE Standard Shipping!

Description

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

Reviews

Section 4.2 on the apply family of functions and related functions for matrices, arrays, and data frames is by far the most friendly and helpful introduction to the subject that I have seen. … All datasets, along with the R-code in the book, are available on the website for the text. … If you are not a trained programmer but you aspire to write code that is efficient and perhaps, from time to time, clever, then this book is a fine place for you to start learning R.

—Homer S. White, MAA Reviews, January 2013

[T]he book is accessible for statisticians of all levels and areas of expertise as well as for novice and advanced R users. … I recommend it for anyone who wants to learn about the why and how of the most commonly employed statistical methods and their extensions.

—Irina Kukuyeva, Journal of Statistical Software, Vol. 51, November 2012

Table of 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

About the Originator

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General