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.
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.
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.
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.
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.
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.
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