Pierre-Andre Cornillon, Arnaud Guyader, Francois Husson, Nicolas Jegou, Julie Josse, Maela Kloareg, ric Matzner-Lober, Laurent Rouvière
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
An Overview of R: Main Concepts. Preparing Data. R Graphics. Making Programs with R. Statistical Methods: Introduction to the Statistical Methods. A Quick Start with R
Hypothesis Test. Regression. Analysis of Variance and Covariance. Classification
Exploratory Multivariate Analysis. Clustering. Appendix.
An Overview of R: Main Concepts. Preparing Data. R Graphics. Making Programs with R. Statistical Methods: Introduction to the Statistical Methods. A Quick Start with RHypothesis Test. Regression. Analysis of Variance and Covariance. ClassificationExploratory Multivariate Analysis. Clustering. Appendix.
This text explores the use of R for classical statistical analysis. The first half of the book introduces R, data manipulation and visualization, statistical models, graphics, and R programming. The second half presents various statistical analysis techniques by first introducing the data example, then describing the problem to solve, and finally conducting the analysis using R. This example-based approach enables readers to replicate the analyses using their own data. Some of the techniques covered include simple regression, multiple regression, ANOVA, logistic regression, principal component analysis, and clustering.
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
Inhaltsverzeichnis
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
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