(Zentralblatt Math, 1 August ). “Overall, The R Book (Second Edition) is a great guide to the vastly powerful and constantly evolving software that is R. It is. Format: HardcoverVerified download. I have the second edition of this book. If you are a beginner to R, this book can be frustrating. The author has not presented. Editorial Reviews. Review. “Overall, The R Book (Second Edition) is a great guide to the vastly powerful and constantly evolving software that is R. It is very close.

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Second Edition. Michael J. Crawley. Imperial College London at Silwood Park, UK tvnovellas.info Hugely successful and popular text presenting an extensive and comprehensive guide for all R users. The R language is recognized as one of the most powerful. Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful.

Crawley was an ambitious work, but managed to be slightly rubbish due to the atrocious typographical layout of the original book. The good news is that the new 2nd edition, released in , has a substantially improved layout that makes the book far more useful as a general reference. This is important, since the book is meant as an accessible reference for non-statisticians to many of the powerful data manipulation and statistical techniques available in R, particularly for biologists and researchers in similar fields. Up to now, the only way to make the 1st edition marginally useful was to seek out one of the pdf versions floating around the nether regions of the web so that you could use the search function to find content. Now that Wiley and Sons has presumably fired the original editor and layout people responsible for the 1st edition due to their massive incompetence, the 2nd edition allows The R Book to actually achieve that goal of being a handy reference. It might finally be one of the first books you pluck off your shelf when you need to figure out how to do something with R.

Nonlinear Parameter Optimization with R explores the principal tools available in R for function minimization, optimization, and nonlinear parameter determination and features numerous examples throughout.

Statistics: An Introduction using R. Wiley, 2nd edition, Ellipses, 1st edition, Psychologie statistique avec R. Pratique R. Springer, Paris, Although many authors have recently advocated for the use of bayesian statistics in psychology Wagenmaker et al.

This manual provides a full bayesian toolbox for commonly encountered problems in psychology and social sciences, for comparing proportions, variances and means, and discusses the advantages. But all foundations of the frequentist approach are also provided, from data description to probability and density, through combinatorics and set algebra.

A special emphasis has been put on the analysis of categorical data and contingency tables. Binomial and multinomial models with beta and Dirichlet priors are presented, and their use for making between rows or between cells contrasts in contingency tables is detailed on real data.

In addition to classical and Bayesian inference on means, direct and Bayesian inference on effect size and standardized effects are presented, in agreement with recent APA recommendations. Dynamic Documents with R and knitr.

The reports range from homework, projects, exams, books, blogs, and web pages to any documents related to statistical graphics, computing, and data analysis.

For beginners, the text provides enough features to get started on basic applications. For power users, the last several chapters enable an understanding of the extensibility of the knitr package. Learn R in a Day. SJ Murray, The book assumes no prior knowledge of computer programming and progressively covers all the essential steps needed to become confident and proficient in using R within a day.

Topics include how to input, manipulate, format, iterate loop , query, perform basic statistics on, and plot data, via a step-by-step technique and demonstrations using in-built datasets which the reader is encouraged to replicate on their computer. Each chapter also includes exercises with solutions to practice key skills and empower the reader to build on the essentials gained during this introductory course. It focuses on scalar financial time series with applications.

High-frequency data and volatility models are discussed. The book also uses case studies to illustrate the application of modeling financial data.

Analyse von Genexpressionsdaten mit R und Bioconductor. Ventus Publishing ApS, London, See web site , March R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating.

Introductory R is written for the novice user who knows a bit about statistics but who hasn't yet got to grips with the ways of R. This book: walks you through the basics of R's command line interface; gives a set of simple rules to follow to make sure you import your data properly; introduces the script editor and gives advice on workflow; contains a detailed introduction to drawing graphs in R and gives advice on how to deal with some of the most common errors that you might encounter.

The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.

Methods of Statistical Model Estimation. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling.

Fully developed, working R code is constructed for each method. The book starts with OLS regression and generalized linear models, building to two-parameter maximum likelihood models for both pooled and panel models. It then covers a random effects model estimated using the EM algorithm and concludes with a Bayesian Poisson model using Metropolis-Hastings sampling. The book's coverage is innovative in several ways. First, the authors use executable computer code to present and connect the theoretical content.

Therefore, code is written for clarity of exposition rather than stability or speed of execution. Second, the book focuses on the performance of statistical estimation and downplays algebraic niceties. In both senses, this book is written for people who wish to fit statistical models and understand them.

Introduction to R for Quantitative Finance. Packt Publishing, November Each chapter briefly presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples. Reproducible Research with R and RStudio.

Suitable for researchers in any quantitative empirical discipline, it presents practical tools for data collection, data analysis, and the presentation of results. The book takes you through a reproducible research workflow, showing you how to use: R for dynamic data gathering and automated results presentation knitr for combining statistical analysis and results into one document LaTeX for creating PDF articles and slide shows, and Markdown and HTML for presenting results on the web Cloud storage and versioning services that can store data, code, and presentation files; save previous versions of the files; and make the information widely available Unix-like shell programs for compiling large projects and converting documents from one markup language to another RStudio to tightly integrate reproducible research tools in one place.

Use R! Springer, New York, Applied Meta-Analysis with R. Filling this knowledge gap, Applied Meta-Analysis with R shows how to implement statistical meta-analysis methods to real data using R. Drawing on their extensive research and teaching experiences, the authors provide detailed, step-by-step explanations of the implementation of meta-analysis methods using R. Each chapter gives examples of real studies compiled from the literature.

After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced.

The authors then develop analysis code using the appropriate R packages and functions. This systematic approach helps readers thoroughly understand the analysis methods and R implementation, enabling them to use R and the methods to analyze their own meta-data.

Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians even those with little or no experience in using R in public health, medical research, governmental agencies, and the pharmaceutical industry.

Moderni analyza biologickych dat. Linear Models with Correlations in R]. Masaryk University Press, Brno, In Czech. Tedy linearni metody, ktere jsou vhodnym nastrojem analyzy dat s casovymi, prostorovymi a fylogenetickymi zavislostmi v datech.

Text knihy je praktickou priruckou analyzy dat v prostredi jednoho z nejrozsahlejsich statistickych nastroju na svete, volne dostupneho softwaru R.

Je sestaven z 19 vzorove vyresenych a okomentovanych prikladu, ktere byly vybrany tak, aby ukazaly spravnou konstrukci modelu a upozornily na problemy a chyby, ktere se mohou v prubehu analyzy dat vyskytnout. Text je psan jednoduchym jazykem srozumitelnym pro ctenare bez specialniho matematickeho vzdelani. Kniha je predevsim urcena studentum i vedeckym pracovnikum biologickych, zemedelskych, veterinarnich, lekarskych a farmaceutickych oboru, kteri potrebuji korektne analyzovat vysledky svych pozorovani ci experimentu s komplikovanejsi strukturou danou zavislostmi mezi opakovanymi merenimi stejneho subjektu.

Soetaert, J. Cash, and F. Solving Differential Equations in R. Springer, This book deals with the numerical solution of differential equations, a very important branch of mathematics. Our aim is to give a practical and theoretical account of how to solve a large variety of differential equations, comprising ordinary differential equations, initial value problems and boundary value problems, differential algebraic equations, partial differential equations and delay differential equations.

The 2nd edition now has those spaces inserted so that the user-typed code is far more readable, particularly for neophytes. In addition, the 2nd edition now uses colors to differentiate the user-typed commands and the R-generated output. The images above do illustrate one continuing gripe I have about The R Book.

Many other R books wrap their data sets up into a R package that can be downloaded from CRAN and easily loaded into a R session with the library function.

The R Book asks you to first download all of the files, and then manually type out their location when you want to load them, i. In what should be taken as damning with faint praise, I will give Crawley credit for at least including a link to the location of the example data files in the 2nd edition of the book. The files were always available for the 1st edition, the book just failed to mention that fact.

The 2nd edition also adds color to many of the figures. The vast majority of the text in the 2nd edition has remained unchanged from the 1st edition. Page The values in the body of a matrix can only be numbers.

That is a false statement.

Pages This be praise, not quibble. Other views site has several reviews of The R Book. There is a range of opinions from very positive to quite negative. A common complaint is that the material is disorganized. Questions The points I have raised are from a quick glance through the book.