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A Handbook of Statistical Analyses Using R ebook

by Brian S. Everitt,Torsten Hothorn

Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment.

Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary.

A Handbook of Statistical Analyses Using R. Brian S. Everitt and Torsten Hothorn. Reproducibility is a natural re-quirement for textbooks such as the ‘Handbook of Statistical Analyses Using R’ and therefore this book is fully reproducible using an R version greater or equal to . This book is intended as a guide to data analysis with the R system for sta-tistical computing. All analyses and results, including gures and tables, can be reproduced by the reader without having to retype a single line of R code. The data sets presented in this book are collected in a dedicated add-on package called HSAUR accompanying this book.

A Handbook of Statistical Analyses Using R Brian S. Everitt and Torsten Hothorn Preface This book is intended as a guide to data analysis with the R system for statistical computing. In the Handbook we aim to give relatively brief and straightforward descriptions of how to conduct a range of statistical analyses using R. Each chapter deals with the analysis appropriate for one or several data sets.

A Handbook of Statistical Analyses Using R is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.

A handbook of statistical analyses using R, Brian S. 2nd ed. p. cm. Includes bibliographical references and index.

A Handbook of Statistical Analyses Using R (Brian S. Everitt and Torsten Hothorn).

Full recovery of all data can take up to 2 weeks!

The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.

The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis. Three new chapters on quantile regression, missing values, and Bayesian inference.

Author(s) Brian S. Everitt, Torsten Hothorn

Author(s) Brian S. Everitt, Torsten Hothorn. Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. Analysis using r. R summary(wg aov). Df Sum Sq Mean Sq F value Pr( F). CHAPTER 4. Analysis of Variance: Weight Gain, Foster Feeding in Rats, Water. Hardness and Male Egyptian Skulls. Analysis of Variance. . 1 Weight Gain in Rats. 1 221 22. 88 . 269.

R is dynamic, to say the least. More precisely, it is organic, with new functionality and add-on packages appearing constantly. And because of its open-source nature and free availability, R is quickly becoming the software of choice for statistical analysis in a variety of fields.Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary. They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while summarizing the most important features. Numerous figures highlight R's strong graphical capabilities and exercises at the end of each chapter reinforce the techniques and concepts presented. All data sets and code used in the book are available as a downloadable package from CRAN, the R online archive.A Handbook of Statistical Analyses Using R is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.
Yozshunris
When it comes to working with statistics, R is a great tool to have at your disposal. Sadly, there is a shortage of information that closes the gap between the simplistic examples used to learn data analysis with R and the more complicated techniques necessary to use R when working with more complex data sets.

_A Handbook of Statistical Analyses Using R_ sits nicely between the traditional introductory tomes for R (Introductory Statistics with R by Peter Dalgaard, or Statistics: An Introduction using R by Michael J. Crawley being two of the best) and the more advanced single topic texts which have a tendency to focus on one particular modeling technique.

As a workbook, the examples are short enough to be worked through in anywhere from 30 minutes to two hours. And while they often assume that the reader is familiar with certain aspects of statistical analysis, a quick refresher is provided for most topics before the exercises.

As a quick reference used to give examples of how to analyze different types of data, the book stands out for having a diverse set of worked examples that give a great jump start into working with R if you need a sample to get going.

If you work with R long enough, you'll find that you need a variety of reference sources to draw upon. _A Handbook of Statistical Analyses Using R_ is a solid addition to that reference library.
Malodred
I was torn between getting this and the big blue R book that all my colleagues rave about. I still plan on getting it some day, but for my purpose this book really helped me out with my research. I especially needed the chapter on regression tree analysis, and for that I am truly appreciative of this book. It starts off by using examples built into the R program. From there I was able to follow along. With trial and error I was able to adapt the code to use with my data. I would not have passed my thesis defense without this book.
Hugighma
This is a nicely written text with accompanying data and syntax for a new analyst or student to gain exposure to R, which has a pretty steep learning curve (especially when coming from a more graphical statistical package). This covers all the basics, but only skims the surface of R functionality and will not explain how to solve problems. I think it's less a reference "handbook" than an introductory text. I haven't returned to it since I started actively using R and got past the basics.
avanger
Brian Everett has previously written similar handbooks for SAS and SPlus. As R is becoming the language of choice in statistical computing in research particularly academoc research this book is a welcome addition. This book is actually a great booj on statistical methods and covers most of the important modern advances including ANOVA, linear regression, generalized linear models with emphasis on logistic regression, probability density estimation (nonparametric), recursive partitioning (i.e. classification and regression trees), survival analysis, bootstrap methods, longitudinal data analysis including mixed effect linear models and generalized estimating equations, meta analyses, principal component analysis, multidimensional scaling and cluster analysis, In each case the methods are clearly explained, are illustrated using real data for examples using R code that is listed for the student to replicate. results are presented through computer output and graphs. This is a very diverse set of methods covering many topics and expecially those commonly needed in clinical trials. the book also contains a very useful bibliography. unfortunately Bayesian techniques are sorely missing with the only reference to Bayes being Schwarz's Bayesian Information Criterion (BIC) that is used for model comparisons.

This book helps open up sensible techniques thst can be applied to a wide variety of problems that the applied researcher might need. The only major technique that is missing here are the Bayesian hierarchical models that have been used extensively in the medical device arm of the FDA (CDRH) are not covered in this fine text.
Leyl
This book is an accessible, higly readable introduction to the R Language and applications in statistics. I have compared other books in the same category and I can find none that approach this book in its clarity of presentation. I highly recommend this book for anyone who is approaching this subject for the first time.
caster
I like this book, and I learned many handy tricks for R. But I am confused, for instance, about density estimation. In section 7.2.1 authors describe classic kernel density estimator which can be found even on Wikipedia. However, in documentation of density() it is clearly stated that FFT is used, whereas FFT is not mentioned at all in chapter 7.
Xtreem
As mentioned above, this book contains short chapters that you can work through quickly and gain a familiarity with R along with a quick review of classical frequentist statistics. I'm about 1/2 of the way through the book and am happy with it.

There is some requisite for at least a beginners knowledge of R and statistics.
Author:
Brian S. Everitt,Torsten Hothorn
Category:
Mathematics
Subcat:
EPUB size:
1318 kb
FB2 size:
1939 kb
DJVU size:
1544 kb
Language:
Publisher:
Chapman and Hall/CRC; 1 edition (February 17, 2006)
Pages:
304 pages
Rating:
4.1
Other formats:
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