Use R! – serie

R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. Currently, R offers a wide range of functionality for nonlinear regression analysis, but the relevant functions, packages and documentation are scattered across the R environment. This book provides a coherent and unified treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology. R. Subsequent chapters explain the salient features of the main fitting function nls (), the use of model diagnostics, how to deal with various model departures, and carry out hypothesis testing. In the final chapter grouped-data structures, including an example of a nonlinear mixed-effects regression model, are considered.

Simulation has become a basic tool for the practice of applied probability and statistics. The Gibbs Sampler is an especially important, but not widely understood simulation method. With a basic introduction to Monte Carlo simulation that provides enough background in other topics, students can understand the rationale for and use of the Gibbs Sampler as a practical simulation tool.

This book is about using interactive and dynamic plots on a computer screen as part of data exploration and modeling, both alone and as a partner with static graphics and non-graphical computational methods. The area of int- active and dynamic data visualization emerged within statistics as part of research on exploratory data analysis in the late 1960s, and it remains an active subject of research today, as its use in practice continues to grow. It now makes substantial contributions within computer science as well, as part of the growing ?elds of information visualization and data mining, especially visual data mining. The material in this book includes: • An introduction to data visualization, explaining how it di?ers from other types of visualization. • Adescriptionofourtoolboxofinteractiveanddynamicgraphicalmethods. • An approach for exploring missing values in data. • An explanation of the use of these tools in cluster analysis and supervised classi?cation. • An overview of additional material available on the web. • A description of the data used in the analyses and exercises. The book’s examples use the software R and GGobi. R (Ihaka & Gent- man 1996, RDevelopment CoreTeam2006) isafreesoftware environment for statistical computing and graphics; it is most often used from the command line, provides a wide variety of statistical methods, and includes high–quality staticgraphics.RaroseintheStatisticsDepartmentoftheUniversityofAu- land and is now developed and maintained by a global collaborative e?ort.

The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. Coupled with the large variety of easily available packages, it allows access to both well-established and experimental statistical techniques. However techniques that might make sense in other languages are often very ine?cient in R, but, due to R’s ?- ibility, it is often possible to implement these techniques in R. Generally, the problem with such techniques is that they do not scale properly; that is, as the problem size grows, the methods slow down at a rate that might be unexpected. The goal of this book is to present a wide variety of data - nipulation techniques implemented in R to take advantage of the way that R works,ratherthandirectlyresemblingmethodsusedinotherlanguages. Since this requires a basic notion of how R stores data, the ?rst chapter of the book is devoted to the fundamentals of data in R. The material in this chapter is a prerequisite for understanding the ideas introduced in later chapters. Since one of the ?rst tasks in any project involving data and R is getting the data into R in a way that it will be usable, Chapter 2 covers reading data from a variety of sources (text ?les, spreadsheets, ?les from other programs, etc. ), as well as saving R objects both in native form and in formats that other programs will be able to work with.

WhenZhouEnlai,PremierofthePeople'sRepublicofChina(1949-1976),was asked his opinion of the French Revolution (1789-1799) he replied "It's too early to tell", see Rosenberg (1999). I believe that the same can be said about wavelets. Although particular wavelets were discovered many years ago, the substantial body of literature that we might today call 'wavelet theory' began to be established during the 1980s. Wavelets were introduced into statistics during the late 1980s and early 1990s, and they were initially popular in the curve estimation literature. From there they spread in di?erent ways to many areassuchassurvivalanalysis,statisticaltimeseriesanalysis,statisticalimage processing, inverse problems, and variance stabilization. The French Revolution was also the historical backdrop for the introd- tion of Fourier series which itself raised considerable objections from the s- enti?c establishment of the day, see Westheimer (2001). Despite those early objections, we ?nd that, 200 years later, many new Fourier techniques are regularly being invented in many di?erent ?elds. Wavelets are also a true s- enti?c revolution. Some of their interesting features are easy to appreciate: e.g., multiscale, localization, or speed.Other important aspects, such as the unconditional basis property, deserve to be better known. I hope that this book, in some small way, enables the creation of many new wavelet methods.

The book is enriched by numerous programming examples to artificial and real data so that it is ideally suited as an accompanying text book to computer lab classes.The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models.

R is rapidly growing in popularity as the environment of choice for data analysis and graphics both in academia and industry. Lattice brings the proven design of Trellis graphics (originally developed for S by William S. Cleveland and colleagues at Bell Labs) to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level data visualization system that is sufficient for most everyday graphics needs, yet flexible enough to be easily extended to handle demands of cutting edge research. Written by the author of the lattice system, this book describes it in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to 150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics.

State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms.The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online.No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from various sources; (2) statistical modeling of differential gene expression; (3) biological metadata; (4) application of graphs and graph rendering; (5) machine learning for clustering and classification problems; (6) gene set enrichment analysis.Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.

R is a language and environment for data analysis and graphics. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN);

This book aims to explain how to use R to perform morphometrics. Morpho- tric analysis is the study of shape and size variations and covariations and their covariations with other variables. Morphometrics is thus deeply rooted within stat- tical sciences. While most applications concern biology, morphometrics is becoming common tools used in archeological, palaeontological, geographical, or medicine disciplines. Since the recent formalizations of some of the ideas of predecessors, such as D’arcy Thompson, and thanks to the development of computer techno- gies and new ways for appraising shape changes and variation, morphometrics have undergone, and are still undergoing, a revolution. Most techniques dealing with s- tistical shape analysis have been developed in the last three decades, and the number of publications using morphometrics is increasing rapidly. However, the majority of these methods cannot be implemented in available software and therefore prosp- tive students often need to acquire detailed knowledge in informatics and statistics before applying them to their data. With acceleration in the accumulation of me- ods accompanying the emerging science of statistical shape analysis, it is becoming important to use tools that allow some autonomy. R easily helps ful?ll this need. Risalanguage andenvironment forstatisticalcomputingandgraphics. Although there is an increasing number of computer applications that perform morphometrics, using R has several advantages that confer to users considerable power and possible new horizons in a world that requires rapid adaptability.

As an area of statistical application, environmental epidemiology and more speci cally, the estimation of health risk associated with the exposure to - vironmental agents, has led to the development of several statistical methods and software that can then be applied to other scienti c areas. The stat- tical analyses aimed at addressing questions in environmental epidemiology have the following characteristics. Often the signal-to-noise ratio in the data is low and the targets of inference are inherently small risks. These constraints typically lead to the development and use of more sophisticated (and pot- tially less transparent) statistical models and the integration of large hi- dimensional databases. New technologies and the widespread availability of powerful computing are also adding to the complexities of scienti c inves- gation by allowing researchers to t large numbers of models and search over many sets of variables. As the number of variables measured increases, so do the degrees of freedom for in uencing the association between a risk factor and an outcome of interest. We have written this book, in part, to describe our experiences developing and applying statistical methods for the estimation for air pollution health e ects. Our experience has convinced us that the application of modern s- tistical methodology in a reproducible manner can bring to bear subst- tial bene ts to policy-makers and scientists in this area. We believe that the methods described in this book are applicable to other areas of environmental epidemiology, particularly those areas involving spatial{temporal exposures.

Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data.

Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.

Ecology is more quantitative and theory-driven than ever before, and A Primer of Ecology with R combines an introduction to the major theoretical concepts in general ecology with a cutting edge open source tool, the R programming language. Starting with geometric growth and proceeding through stability of multispecies interactions and species-abundance distributions, this book demystifies and explains fundamental ideas in population and community ecology. Graduate students in ecology, along with upper division undergraduates and faculty, will find this to be a useful overview of important topics.In addition to the most basic topics, this book includes construction and analysis of demographic matrix models, metapopulation and source-sink models, host-parasitoid and disease models, multiple basins of attraction, the storage effect, neutral theory, and diversity partitioning. Several sections include examples of confronting models with data. Chapter summaries and problem sets at the end of each chapter provide opportunities to evaluate and enrich one's understanding of the ecological ideas that each chapter introduces.R is rapidly becoming the lingua franca of quantitative sciences, and this text provides a tractable introduction to using the R programming environment in ecology. An appendix provides a general introduction, and examples of code throughout each chapter give readers the option to hone their growing R skills. "The distinctive strength of this book is that truths are mostly not revealed but discovered, in the way that R-savvy ecologists—empirical and theoretical—work and think now. For readers still chained to spreadsheets, working through this book could be a revolution in their approach to doing science." (Stephen P. Ellner, Cornell University)"One of the greatest strengths…is the integration of ecological theory with examples ... pulled straight from the literature." (James R. Vonesh, Virginia Commonwealth University)

There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).

Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes.

Scientists often collect samples of curves and other functional observations, and develop models where parameters are also functions. This volume in the UseR! Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems. It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase functional data analysis techniques. The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the details of key functions within these pages. This book is accompanied by additional web-based support at http://www.functionaldata.org for applying existing functions and developing new ones in either language. The companion 'fda' package for R includes script files to reproduce nearly all the examples in the book including all but one of the 76 figures.

R, a free and open source program, is one of the most powerful and the fastest-growing statistics program. Microsoft Excel is the most widely used spreadsheet program, but many statisticians consider its statistical tools too limited.In this book, the authors build on RExcel, a free add-in for Excel that can be downloaded from the R distribution network. RExcel seamlessly integrates the entire set of R's statistical and graphical methods into Excel, allowing students to focus on statistical methods and concepts and minimizing the distraction of learning a new programming language.Data can be transferred between R and Excel “the Excel way” by selecting worksheet ranges and using Excel menus. R’s basic statistical functions and selected advanced methods are available from an Excel menu. Results of the computations and statistical graphics can be returned back into Excel worksheet ranges. RExcel allows the use of Excel scroll bars and check boxes to create and animate R graphics as an interactive analysis tool.The book is designed as a computational supplement to introductory statistics texts and the authors provide RExcel examples covering the topics of the introductory course.

Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison.

Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. Theauthors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications.The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying andusing forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics,and very basic applied mathematics.

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set.

The focus on doing data mining rather than just reading about data mining is refreshing.The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment.

Business Analytics for Managers helps readers extract knowledge and actionable insight from real business data. The text emphasizes data-driven thinking and provides a quick-start guide to one of the most powerful software solutions available.

This book is written for behavioral scientists who want to consider adding R to their existing set of statistical tools, or want to switch to R as their main computation tool.

Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis.This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text.

The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification.In the second edition, the book continues to integrate a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. The second edition is completed updated, covering the full gamut of R packages for this area that have been introduced  to the market since its previous publication five years ago. There is also  a new chapter on the simulation of evolutionary data.  Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with otherlanguages (C and C++). Some exercises conclude these chapters.

This book covers competing risks and multistate models, sometimes summarized as event history analysis. These models generalize the analysis of time to a single event (survival analysis) to analysing the timing of distinct terminal events (competing risks) and possible intermediate events (multistate models). Both R and multistate methods are promoted with a focus on nonparametric methods.

Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences.  Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years.  In recent years many of these software developments have taken place within the R community, either in the form of new packages or by providing an R interface to existing software.  This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages.  In addition, the book provides examples of how more advanced aspects of graphical modeling can be represented and handled within R.  Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models, Bayesian networks and modeling high dimensional data.