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The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects.

After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models.

They estimate the multivariate generalized linear mixed models MGLMMs using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models.

The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples.

In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies.

Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website.

The Foundations of Statistics: In such fields, when faced with experimental data, many students and researchers tend to rely on commercial packages to carry out statistical data analysis, often without understanding the logic of the statistical tests they rely on.

As a consequence, results are often misinterpreted, and users have difficulty in flexibly applying techniques relevant to their own research they use whatever they happen to have learned.

A simple solution is to teach the fundamental ideas of statistical hypothesis testing without using too much mathematics. This book provides a non-mathematical, simulation-based introduction to basic statistical concepts and encourages readers to try out the simulations themselves using the source code and data provided the freely available programming language R is used throughout.

Since the code presented in the text almost always requires the use of previously introduced programming constructs, diligent students also acquire basic programming abilities in R.

The book is intended for advanced undergraduate and graduate students in any discipline, although the focus is on linguistics, psychology, and cognitive science.

It is designed for self-instruction, but it can also be used as a textbook for a first course on statistics. Earlier versions of the book have been used in undergraduate and graduate courses in Europe and the US.

R for Stata Users. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches.

When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.

The book begins by introducing the R language, including the development environment. As you work through various examples illustrating R's features, you'll also get a crash course in practical statistics, including basic and advanced models for normal and non- normal data, longitudinal and survival data, and a wide variety of multivariate methods.

Both data mining methodologies and approaches to messy and incomplete data are included. Presses Universitaires de Rennes, 2nd edition, Ce livre s'articule en deux grandes parties: R in a Nutshell [deutsche Ausgabe].

O'Reilly Verlag, Köln, 1. Es beschreibt die Installation und Erweiterung der Software und gibt einen breiten Überblick über die Programmiersprache.

Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik behandelt es, wie Daten eingelesen, transformiert und grafisch dargestellt werden.

Anhand realer Datensätze werden zahlreiche Methoden und Verfahren der statistischen Datenanalyse mit R demonstriert.

Die Funktionsreferenz wurde für die deutsche Ausgabe vollständig neu verfasst. Along the way, you will learn how to use R to arrive at practical solutions and how to effectively communicate your results.

Ultimately, the fate of the kingdom depends on your ability to make informed, data- driven decisions with R. Computer Sciences and Data Analysis.

It covers principal component analysis PCA when variables are quantitative, correspondence analysis CA and multiple correspondence analysis MCA when variables are categorical, and hierarchical cluster analysis.

The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualizing objects that are common to the exploratory methods.

The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical.

They also illustrate the methods and the ways they can be exploited using examples from various fields.

Throughout the text, each result correlates with an R command accessible in the FactoMineR package developed by the authors. All of the data sets and code are available at http: By using the theory, examples, and software presented in this book, readers will be fully equipped to tackle real-life multivariate data.

Statistics and Data Analysis for Financial Engineering. Key features of this textbook are: Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.

Some exposure to finance is helpful. Introducing Monte Carlo Methods with R. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems.

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.

While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella Similarly, the more exploratory and less stable solutions are not covered here.

This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required.

The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis-Hastings and Gibbs algorithms, and adaptive algorithms.

All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure.

It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more.

The programming parts are introduced progressively to be accessible to any reader. Clinical Trial Data Analysis with R. Filling this gap, Clinical Trial Data Analysis Using R provides a thorough presentation of biostatistical analyses of clinical trial data and shows step by step how to implement the statistical methods using R.

The book's practical, detailed approach draws on the authors' 30 years of real-world experience in biostatistical research and clinical development.

Each chapter presents examples of clinical trials based on the authors' actual experiences in clinical drug development.

Various biostatistical methods for analyzing the data are then identified. The authors develop analysis code step by step using appropriate R packages and functions.

This approach enables readers to gain an understanding of the analysis methods and R implementation so that they can use R to analyze their own clinical trial data.

With step-by-step illustrations of R implementations, this book shows how to easily use R to simulate and analyze data from a clinical trial.

It describes numerous up-to-date statistical methods and offers sound guidance on the processes involved in clinical trials.

Spatial Statistics and Modeling. Springer Series in Statistics. This book covers the best-known spatial models for three types of spatial data: The level is relatively advanced, and the presentation concise but complete.

The most important statistical methods and their asymptotic properties are described, including estimation in geostatistics, autocorrelation and second-order statistics, maximum likelihood methods, approximate inference using the pseudo-likelihood or Monte-Carlo simulations, statistics for point processes and Bayesian hierarchical models.

A large number of real examples are studied with R, and each chapter ends with a set of theoretical and applied exercises.

While a foundation in probability and mathematical statistics is assumed, three appendices introduce some necessary background. The book is accessible to senior undergraduate students with a solid math background and Ph.

Furthermore, experienced statisticians and researchers in the above-mentioned fields will find the book valuable as a mathematically sound reference.

Forest Analytics with R. The authors 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 and using 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.

Lecture Notes in Statistics. The following twelve chapters written by distinguished authors cover a wide range of issues--all providing practical tools using the free R software.

R can be used for reliable statistical computing, whereas most statistical and econometric software cannot. This is illustrated by the effect of abortion on crime.

Additive models provide a clever compromise between parametric and non-parametric components illustrated by risk factors for Indian malnutrition.

R graphics in the context of voter participation in US elections. New solutions to the old problem of efficient estimation despite autocorrelation and heteroscedasticity among regression errors are proposed and illustrated by the Phillips curve tradeoff between inflation and unemployment.

New R tools for exploratory data analysis including bubble plots. Vinod, Hsu and Tian: New R tools for portfolio selection borrowed from computer scientists and data-mining experts, relevant to anyone with an investment portfolio.

Imai, Keele, Tingley, and Yamamoto: New R tools for solving the age-old scientific problem of assessing the direction and strength of causation. Their job search illustration is of interest during current times of high unemployment.

Haupt, Schnurbus, and Tschernig: R methods to fit a multinomial based multivariate analysis of variance ANOVA with examples from psychology, sociology, political science, and medicine.

R tools for Bayesian posterior distributions to study increased disease risk in proximity to a hazardous waste site. An Introduction Using R.

It introduces tools to enable readers to learn and use fundamental methods for constructing quantitative models of biological mechanisms, both deterministic and with some elements of randomness, including complex reaction equilibria and kinetics, population models, and regulation of metabolism and development; to understand how concepts of probability can help in explaining important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data from spectroscopic, genomic, and proteomic sources.

These quantitative tools are implemented using the free, open source software program R. R provides an excellent environment for general numerical and statistical computing and graphics, with capabilities similar to Matlab.

Since R is increasingly used in bioinformatics applications such as the BioConductor project, it can serve students as their basic quantitative, statistical, and graphics tool as they develop their careers [ 75 ] Uwe Ligges.

Springer-Verlag, Heidelberg, 3rd edition, Die enormen Grafikfähigkeiten von R werden detailliert beschrieben. Der Leser kann leicht eigene Methoden umsetzen, Objektklassen definieren und ganze Pakete aus Funktionen und zugehöriger Dokumentation zusammenstellen.

Ob Diplomarbeit, Forschungsprojekte oder Wirtschaftsdaten, das Buch unterstützt alle, die R als flexibles Werkzeug zur Datenanalyse und -visualisierung einsetzen möchten.

Generalised Linear Models in R]. Je urcena predevsim studentum a kolegum z biologickych oboru a vyzaduje pouze zakladni statisticke vzdelani, jakym je napr.

Text knihy obsahuje nezbytne minimum statisticke teorie, predevsim vsak reseni 18 realnych prikladu z oblasti biologie. Kazdy priklad je rozpracovan od popisu a stanoveni cile pres vyvoj statistickeho modelu az po zaver.

K analyze dat je pouzit popularni a volne dostupny statisticky software R. Priklady byly zamerne vybrany tak, aby upozornily na lecktere problemy a chyby, ktere se mohou v prubehu analyzy dat vyskytnout.

Zaroven maji ctenare motivovat k tomu, jak o statistickych modelech premyslet a jak je pouzivat. Reseni prikladu si muse ctenar vyzkouset sam na datech, jez jsou dodavana spolu s knihou.

Springer Series in Statistics and Computing. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches.

The programs and practice datasets are available for download. Heiberger and Erich Neuwirth. The presentation is designed as a computational supplement to introductory statistics texts.

The authors provide RExcel examples for most topics in the introductory course. Data can be transferred from Excel to R and back. The clickable RExcel menu supplements the powerful R command language.

Results from the analyses in R can be returned to the spreadsheet. Ordinary formulas in spreadsheet cells can use functions written in R.

Discussions of the development, implementation, and applications of this technology are available at http: The book is accessible to readers with only a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods.

R code is provided throughout the text. Cowpertwait and Andrew Metcalfe. Introductory Time Series with R. 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 confirms understanding of both the model and the R routine for fitting it to the data. Finally, the model is applied to an analysis of a historical data set.

By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http: The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyze time series as part of their taught program or their research.

Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems.

A Primer of Ecology with R. 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 all find this to be a useful overview of important topics.

Introduction to Multivariate Statistical Analysis in Chemometrics. It includes discussions of various statistical methods, such as principal component analysis, regression analysis, classification methods, and clustering.

Written by a chemometrician and a statistician, the book reflects both the practical approach of chemometrics and the more formally oriented one of statistics.

To enable a better understanding of the statistical methods, the authors apply them to real data examples from chemistry.

They also examine results of the different methods, comparing traditional approaches with their robust counterparts.

In addition, the authors use the freely available R package to implement methods, encouraging readers to go through the examples and adapt the procedures to their own problems.

Focusing on the practicality of the methods and the validity of the results, this book offers concise mathematical descriptions of many multivariate methods and employs graphical schemes to visualize key concepts.

It effectively imparts a basic understanding of how to apply statistical methods to multivariate scientific data.

Broman and Saunak Sen. Two moderately challenging case studies illustrate QTL analysis in its entirety. Novice readers will find detailed explanations of the important statistical concepts and, through the extensive software illustrations, will be able to apply these concepts in their own research.

Mathematical Modeling and Simulation: Introduction for Scientists and Engineers. Requiring only little mathematical prerequisite in calculus and linear algebra, it is accessible to scientists, engineers, and students at the undergraduate level.

Bayesian Computation with R. Springer, 2nd edition, The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems.

Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model.

These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling.

Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution.

The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner's g priors to choose between models in linear regression.

There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions.

The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package.

Ramsay, Giles Hooker, and Spencer Graves. Functional Data Analysis with R and Matlab. Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems.

Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase the functional data analysis.

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: Elegant Graphics for Data Analysis. You will need some basic knowledge of R i.

After reading this book you'll be able to produce graphics customized precisely for your problems, to and you'll find it easy to get graphics out of your head and on to the screen or page.

Includes bibliographical references and index. An Introduction to R illustrates how to use the freely available R software package for data analysis, statistical programming, and graphics.

Integrating R code and examples throughout, the text only requires basic knowledge of statistics and computing. This introduction covers one-sample analysis and distribution diagnostics, regression, two-sample problems and comparison of distributions, and multivariate analysis.

It uses a range of examples to demonstrate how R can be employed to tackle statistical problems. In addition, the handy appendix includes a collection of R language elements and functions, serving as a quick reference and starting point to access the rich information that comes bundled with R.

Accessible to a broad audience, this book explores key topics in data analysis, regression, statistical distributions, and multivariate statistics.

Full of examples and with a color insert, it helps readers become familiar with R. Dynamic Linear Models with R. 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.

Presses Universitaires de Rennes, Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research.

Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or done simplistically, and updating of previously developed models is not considered.

A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. Clinical prediction models presents a practical checklist with seven steps that need to be considered for development of a valid prediction model.

These include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formats.

The steps are illustrated with many small case-studies and R code, with data sets made available in the public domain.

The book further focuses on generalizability of prediction models, including patterns of invalidity that may be encountered in new settings, approaches to updating of a model, and comparisons of centers after case-mix adjustment by a prediction model.

The text is primarily intended for clinical epidemiologists and biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis.

It is beneficial if readers are familiar with common statistical models in medicine: The book is practical in nature. But it provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling.

In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy.

Prediction models are key to individualizing diagnostic and treatment decision making. Verlag Detlev Reymann, Geisenheim, Dabei stehen folgende Teilanalysen im Mittelpunkt: Zu jeder der Teilanalysen werden nach der Darstellung der theoretischen Grundlagen Hinweise und Anleitungen zur praktischen Umsetzung und Durchführung gegeben und jeweils eine vertiefende Betrachtung angeschlossen.

Verwendet werden Verfahren, bei denen sich zum einen der zeitliche Aufwand für die Durchführung in vertretbaren Grenzen hält, zum anderen Analysen, die mit Hilfe von frei verfügbarer Software oder frei verfügbaren Daten durchzuführen sind.

Für den Statistikteil werden R-Skripte verwendet, die alle frei von der Webseite des Autors heruntergeladen werden können. Im Kapitel zur Kundentypologisierung wird die Durchführung von Cluster- und Faktoranlysen zur Typologisierung erläutert und der Anhang gibt Hinweise zur Installation und zum Einsatz von R für die beschriebenen Analysen.

Wright and Kamala London. Modern Regression Techniques Using R: These are all tested out using a range of real research examples conducted by the authors in every chapter, and datasets are available from the book's web page at https: The authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.

Nonlinear Regression with R. 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.

The book starts out giving a basic introduction to fitting nonlinear regression models in 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.

Applied Statistical Genetics with R: For Population-Based Association Studies. Foulkes elucidates core concepts that undergird the wide range of analytic techniques and software tools for the analysis of data derived from population-based genetic investigations.

Applied Statistical Genetics with R offers a clear and cogent presentation of several fundamental statistical approaches that researchers from multiple disciplines, including medicine, public health, epidemiology, statistics and computer science, will find useful in exploring this emerging field.

As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout. The second part provides ten case studies that range from koalas to deep sea research.

These chapters provide an invaluable insight into analysing complex ecological datasets, including comparisons of different approaches to the same problem.

By matching ecological questions and data structure to a case study, these chapters provide an excellent starting point to analysing your own data.

Data and R code from all chapters are available from http: Ieno, and Erik Meesters. A Beginner's Guide to R.

To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. 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.

This book contains everything you need to know to get started with R. Simulation and Inference for Stochastic Differential Equations: The book should be useful to practitioners and students with minimal mathematical background, but because of the many R programs, probably also to many mathematically well educated practitioners.

Many of the methods presented in the book have, so far, not been used much in practice because the lack of an implementation in a unified framework.

This book fills the gap. With the R code included in this book, a lot of useful methods become easy to use for practitioners and students.

Although it contains a wide range of results, the book has an introductory character and necessarily does not cover the whole spectrum of simulation and inference for general stochastic differential equations.

The book is organized in four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences.

The second chapter is devoted to simulation schemes and covers new methods not available in other milestones publication known so far.

The third one is focused on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments and other techniques.

The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation. The reader non-expert in R language, will find a concise introduction to this environment focused on the subject of the book which should allow for instant use of the proposed material.

To each R functions presented in the book a documentation page is available at the end of the book. A Modern Approach to Regression with R.

When weaknesses in the model are identified, the next step is to address each of these weaknesses. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models.

The regression output and plots that appear throughout the book have been generated using R. On the book website you will find the R code used in each example in the text.

The book contains a number of new real data sets from applications ranging from rating restaurants, rating wines, predicting newspaper circulation and magazine revenue, comparing the performance of NFL kickers, and comparing finalists in the Miss America pageant across states.

One of the aspects of the book that sets it apart from many other regression books is that complete details are provided for each example.

The book is aimed at first year graduate students in statistics and could also be used for a senior undergraduate class.

Multivariate Data Visualization with R. 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 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.

Applied Spatial Data Analysis with R. This part is of interest to users who need to access and visualise spatial data.

The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping.

The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature.

All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website.

This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis.

It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science.

The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: Peng and Francesca Dominici.

Statistical Methods for Environmental Epidemiology with R: The methods and software developed in this area are applicable to a wide array of problems in environmental epidemiology.

This book provides an overview of the methods used for investigating the health effects of air pollution and gives examples and case studies in R which demonstrate the application of those methods to real data.

The book will be useful to statisticians, epidemiologists, and graduate students working in the area of air pollution and health and others analyzing similar data.

The authors describe the different existing approaches to statistical modeling and cover basic aspects of analyzing and understanding air pollution and health data.

The case studies in each chapter demonstrate how to use R to apply and interpret different statistical models and to explore the effects of potential confounding factors.

A working knowledge of R and regression modeling is assumed. In-depth knowledge of R programming is not required to understand and run the examples.

Software for all of the analyses in the book is downloadable from the web and is available under a Free Software license.

The reader is free to run the examples in the book and modify the code to suit their needs. With the database, readers can run the examples and experiment with their own methods and ideas.

R Programming for Bioinformatics. R Programming for Bioinformatics builds the programming skills needed to use R for solving bioinformatics and computational biology problems.

Drawing on the author's experiences as an R expert, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R.

It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages.

He concludes with a discussion on the debugging and profiling of R code. Data Manipulation with R. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics.

However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R.

This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data.

All of the methods presented take advantage of the core features of R: Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R.

Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs.

Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R.

For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided.

Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book.

Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions.

Springer, New York, 2nd edition, This book not only introduces the reader to this topic but enables him to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R.

The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and multivariate time series models.

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.

To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting.

The author explains how these model types relate to each other. He obtained a diploma and a doctorate degree at the economics department of the latter entity where he was employed as a research and teaching assistant.

Introductory Statistics with R. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint.

A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations.

In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression.

Statistical Computing with R. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts.

Semiparametric Regression for the Social Sciences. Semiparametric Regression for the Social Sciences sets out to address this situation by providing an accessible introduction to the subject, filled with examples drawn from the social and political sciences.

Readers are introduced to the principles of nonparametric smoothing and to a wide variety of smoothing methods. The author also explains how smoothing methods can be incorporated into parametric linear and generalized linear models.

The use of smoothers with these standard statistical models allows the estimation of more flexible functional forms whilst retaining the interpretability of parametric models.

The full potential of these techniques is highlighted via the use of detailed empirical examples drawn from the social and political sciences.

Each chapter features exercises to aid in the understanding of the methods and applications. All examples in the book were estimated in R. The book contains an appendix with R commands to introduce readers to estimating these models in R.

All the R code for the examples in the book are available from the author's website and the publishers website. Cryer and Kung-Sik Chan.

Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models.

All of the ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment.

The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text.

Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses.

Software for Data Analysis: This book guides the reader in programming with R, from interactive use and writing simple functions to the design of R packages and intersystem interfaces.

Hands-on Intermediate Econometrics Using R: Templates for Extending Dozens of Practical Examples. World Scientific, Hackensack, NJ, It helps readers choose the best method from a wide array of tools and packages available.

The data used in the examples along with R program snippets, illustrate the economic theory and sophisticated statistical methods extending the usual regression.

The R program snippets are included on a CD accompanying the book. These are not merely given as black boxes, but include detailed comments which help the reader better understand the software steps and use them as templates for possible extension and modification.

The book has received endorsements from top econometricians. Wavelet Methods in Statistics with R. This book fulfils three purposes.

First, it is a gentle introduction to wavelets and their uses in statistics. Second, it acts as a quick and broad reference to many recent developments in the area.

The book concentrates on describing the essential elements and provides comprehensive source material references.

Third, the book intersperses R code that explains and demonstrates both wavelet and statistical methods. The code permits the user to learn the methods, to carry out their own analyses and further develop their own methods.

The book is designed to be read in conjunction with WaveThresh4, the freeware R package for wavelets. The book introduces the wavelet transform by starting with the simple Haar wavelet transform and then builds to consider more general wavelets such as the Daubechies compactly supported series.

The book then describes the evolution of wavelets in the directions of complex-valued wavelets, non-decimated transforms, multiple wavelets and wavelet packets as well as giving consideration to boundary conditions initialization.

Later chapters explain the role of wavelets in nonparametric regression problems via a variety of techniques including thresholding, cross-validation, SURE, false-discovery rate and recent Bayesian methods, and also consider how to deal with correlated and non-Gaussian noise structures.

The book also looks at how nondecimated and packet transforms can improve performance. The penultimate chapter considers the role of wavelets in both stationary and non-stationary time series analysis.

The final chapter describes recent work concerning the role of wavelets for variance stabilization for non-Gaussian intensity estimation. The book is aimed at final year undergraduate and Masters students in a numerate discipline such as mathematics, statistics, physics, economics and engineering and would also suit as a quick reference for postgraduate or research level activity.

The book would be ideal for a researcher to learn about wavelets, to learn how to use wavelet software and then to adapt the ideas for their own purposes.

Statistical Data Analysis Explained: Applied Environmental Statistics with R. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead.

To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences.

The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations geographic coordinates , which leads to the necessity of using maps to display the data and the results of the statistical methods.

Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.

The book is unique because it supplies direct access to software solutions based on R, the Open Source version of the S-language for statistics for applied environmental statistics.

For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts.

Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.

The R language and environment offers a single platform to perform a multitude of analyses from the acquisition of data to the production of static and interactive graphs.

This offers an ideal environment to analyze shape variation and shape change. This open-source language is accessible for novices and for experienced users.

Adopting R gives the user and developer several advantages for performing morphometrics: The book explains how to use R for morphometrics and provides a series of examples of codes and displays covering approaches ranging from traditional morphometrics to modern statistical shape analysis such as the analysis of landmark data, Thin Plate Splines, and Fourier analysis of outlines.

The book fills two gaps: Students and scientists interested in shape analysis can use the book as a reference for performing applied morphometrics, while prospective researchers will learn how to implement algorithms or interfacing R for new methods.

In addition, adopting the R philosophy will enhance exchanges within and outside the morphometrics community. Julien Claude is evolutionary biologist and palaeontologist at the University of Montpellier 2 where he got his Ph.

He works on biodiversity and phenotypic evolution of a variety of organisms, especially vertebrates. He teaches evolutionary biology and biostatistics to undergraduate and graduate students and has developed several functions in R for the package APE.

Applied Econometrics with R. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions.

In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research.

It contains some data sets taken from a wide variety of sources, the full source code for all examples used in the text plus further worked examples, e.

The data sets are suitable for illustrating, among other things, the fitting of wage equations, growth regressions, hedonic regressions, dynamic regressions and time series models as well as models of labor force participation or the demand for health care.

The goal of this book is to provide a guide to R for users with a background in economics or the social sciences.

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The properties leaves