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[2009, book| url]

Wright, D. (2009). Modern Regression Techniques Using R: A Practical Guide SAGE.

Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. 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 http://www.uk.sagepub.com/booksProdSampleMaterials.nav?prodId=Book233198. The authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.

[2009, book| url]

Ligges, U. (2009). Programmieren mit R. (3rd ed.). Heidelberg: Springer-Verlag.

ist eine objekt-orientierte und interpretierte Sprache und Programmierumgebung f\\”ur Datenanalyse und Grafik — frei erh\\”altlich unter der GPL. Das Buch f\\”uhrt in die Grundlagen der Sprache ein und vermittelt ein umfassendes Verst\\”andnis der Sprachstruktur. Die enormen Grafikf\\”ahigkeiten von werden detailliert beschrieben. Der Leser kann leicht eigene Methoden umsetzen, Objektklassen definieren und ganze Pakete aus Funktionen und zugeh\\”origer Dokumentation zusammenstellen. Ob Diplomarbeit, Forschungsprojekte oder Wirtschaftsdaten, das Buch unterst\\”utzt alle, die als flexibles Werkzeug zur Datenanalyse und -visualisierung einsetzen m\\”ochten.

[2009, book| url]

Pekar, S., & Brabec, M. (2009). Moderni analyza biologickych dat. 1. Zobecnene linearni modely v prostredi R [Modern Analysis of Biological Data. 1. Generalised Linear Models in R]. Praha: Scientia.

Kniha je zamerena na regresni modely, konkretne jednorozmerne zobecnene linearni modely (GLM). Je urcena predevsim studentum a kolegum z biologickych oboru a vyzaduje pouze zakladni statisticke vzdelani, jakym je napr. jednosemestrovy kurz biostatistiky. 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 . 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.

[2009, book| url]

Muenchen, R. A. (2009). R for SAS and SPSS Users. Springer.

This book demonstrates which of the add-on packages are most like SAS and SPSS and compares them to ‘s built-in functions. 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.

[2009, book| url]

Heiberger, R. M., & Neuwirth, E. (2009). R Through Excel. Springer.

The primary focus of the book is on the use of menu systems from the Excel menu bar into the capabilities provided by . The presentation is designed as a computational supplement to introductory statistics texts. The authors provide Excel examples for most topics in the introductory course. Data can be transferred from Excel to and back. The clickable Excel menu supplements the powerful command language. Results from the analyses in can be returned to the spreadsheet. Ordinary formulas in spreadsheet cells can use functions written in . Discussions of the development, implementation, and applications of this technology are available at http://rcom.univie.ac.at/.

[2009, book| url]

Hoff, P. D. (2009). A First Course in Bayesian Statistical Methods. Springer.

This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. 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. code is provided throughout the text. Much of the example code can be run ``as is’’ in , and essentially all of it can be run after downloading the relevant datasets from the companion website for this book.

[2009, book| url]

Cowpertwait, P. S. P., & Metcalfe, A. (2009). Introductory Time Series with R. Springer.

This book gives you a step-by-step introduction to analysing time series using the open source software . Once the model has been introduced it is used to generate synthetic data, using code, and these generated data are then used to estimate its parameters. This sequence confirms understanding of both the model and the routine for fitting it to the data. Finally, the model is applied to an analysis of a historical data set. By using , the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://www.massey.ac.nz/~pscowper/ts. 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.

[2009, book| url]

Stevens, H. M. H. (2009). A Primer of Ecology with R. Springer.

This book combines an introduction to the major theoretical concepts in general ecology with the programming language , a cutting edge Open Source tool. 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.

[2009, book| url]

Varmuza, K., & Filzmoser, P. (2009). Introduction to Multivariate Statistical Analysis in Chemometrics. Boca Raton, FL: CRC Press.

Using formal descriptions, graphical illustrations, practical examples, and software tools, Introduction to Multivariate Statistical Analysis in Chemometrics presents simple yet thorough explanations of the most important multivariate statistical methods for analyzing chemical data. 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 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.

[2009, book| url]

Broman, K. W., & Sen, S. (2009). A Guide to QTL Mapping with R/qtl. Springer.

This book is a comprehensive guide to the practice of QTL mapping and the use of /qtl, including study design, data import and simulation, data diagnostics, interval mapping and generalizations, two-dimensional genome scans, and the consideration of complex multiple-QTL models. Two moderately challenging case studies illustrate QTL analysis in its entirety. The book alternates between QTL mapping theory and examples illustrating the use of /qtl. 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. Experienced readers will find details on the underlying algorithms and the implementation of extensions to /qtl.

[2009, book| url]

Velten, K. (2009). Mathematical Modeling and Simulation: Introduction for Scientists and Engineers. Wiley-VCH.

This introduction into mathematical modeling and simulation is exclusively based on open source software, and it includes many examples from such diverse fields as biology, ecology, economics, medicine, agricultural, chemical, electrical, mechanical, and process engineering. Requiring only little mathematical prerequisite in calculus and linear algebra, it is accessible to scientists, engineers, and students at the undergraduate level. The reader is introduced into CAELinux, Calc, Code-Saturne, Maxima, , and Salome-Meca, and the entire book software — including 3D CFD and structural mechanics simulation software — can be used based on a free CAELinux-Live-DVD that is available in the Internet (works on most machines and operating systems).

[2009, book| url]

Albert, J. (2009). Bayesian Computation with R. (2nd ed.) Springer.

Bayesian Computing Using introduces Bayesian modeling by the use of computation using the language. 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. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. 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 are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of to interface with WinBuGS, a popular MCMC computing language, is described with several illustrative examples. 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 code illustrations according to the latest edition of the LearnBayes package.

[2009, book| url]

Ramsay, J. O., Hooker, G., & Graves, S. (2009). Functional Data Analysis with R and Matlab. Springer.

This volume in the Use ! 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 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://www.functionaldata.org for applying existing functions and developing new ones in either language.

[2009, book| url]

Wickham, H. (2009). Ggplot: Elegant Graphics for Data Analysis. Springer.

This book will be useful to everyone who has struggled with displaying their data in an informative and attractive way. You will need some basic knowledge of (i.e., you should be able to get your data into ), but ggplot2 is a mini-language specifically tailored for producing graphics, and you’ll learn everything you need in the book. 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.

[2009, book| url]

Petris, G., Petrone, S., & Campagnoli, P. (2009). Dynamic Linear Models with R. Springer.

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 . 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 is assumed.

[2009, book| url]

Reymann, D. (2009). Wettbewerbsanalysen f\ür kleine und mittlere Unternehmen (KMUs) --- Theoretische Grundlagen und praktische Anwendung am Beispiel gartenbaulicher Betriebe. Geisenheim: Verlag Detlev Reymann.

In diesem Buch werden die Grundlagen wesentlicher Komponenten von unternehmens- und konkurrentenbezogenen Wettbewerbsanalysen dargestellt. Dabei stehen folgende Teilanalysen im Mittelpunkt: Die Analyse des Einzugsgebietes; die Ermittlung des Marktpotentials und des Marktanteiles; die Ermittlung der St\\”arken und Schw\\”achen im Verh\\”altnis zur Konkurrenz; die Analyse der Kundenstruktur (Kundentypologisierung). Zu jeder der Teilanalysen werden nach der Darstellung der theoretischen Grundlagen Hinweise und Anleitungen zur praktischen Umsetzung und Durchf\\”uhrung gegeben und jeweils eine vertiefende Betrachtung angeschlossen. Das Buch zielt insbesondere auf kleine und mittlere Unternehmen (KMUs) ab, in denen keine gro\\ss{}en spezialisierten Marketingabteilungen existieren. Verwendet werden Verfahren, bei denen sich zum einen der zeitliche Aufwand f\\”ur die Durchf\\”uhrung in vertretbaren Grenzen h\\”alt, zum anderen Analysen, die mit Hilfe von frei verf\\”ugbarer Software oder frei verf\\”ugbaren Daten durchzuf\\”uhren sind. F\\”ur den Statistikteil werden R-Skripte verwendet, die alle frei von der Webseite des Autors heruntergeladen werden k\\”onnen. Es handelt sich dabei um Skripte zur Berechnung des breaking-points nach Converse, zur Berechnung der Einkaufswahrscheinlichkeit nach Huff und zur Erstellung von Profildiagrammen im Rahmen von SWOT-Analysen sowie von Imageprofilen. Im Kapitel zur Kundentypologisierung wird die Durchf\\”uhrung von Cluster- und Faktoranlysen zur Typologisierung erl\\”autert und der Anhang gibt Hinweise zur Installation und zum Einsatz von f\\”ur die beschriebenen Analysen.

[2009, book| url]

Ritz, C., & Streibig, J. C. (2009). Nonlinear Regression with R. New York: Springer.

is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. Currently, offers a wide range of functionality for nonlinear regression analysis, but the relevant functions, packages and documentation are scattered across the environment. This book provides a coherent and unified treatment of nonlinear regression with 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 . 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.

[2009, book| url]

Foulkes, A. S. (2009). Applied Statistical Genetics with R: For Population-Based Association Studies. Springer.

In this introductory graduate level text, Dr.~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 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.

[2009, book| url]

Zuur, A., Ieno, E. N., Walker, N., Saveiliev, A. A., & Smith, G. M. (2009). Mixed Effects Models and Extensions in Ecology with R. New York: Springer.

Building on the successful Analysing Ecological Data (2007) by Zuur, Ieno and Smith, the authors now provide an expanded introduction to using regression and its extensions in analysing ecological data. As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout. The first part of the book is a largely non-mathematical introduction to linear mixed effects modelling, GLM and GAM, zero inflated models, GEE, GLMM and GAMM. 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 code from all chapters are available from http://www.highstat.com.

[2009, book| url]

Zuur, A. F., Ieno, E. N., & Meesters, E. (2009). A Beginner's Guide to R. Springer.

Based on their extensive experience with teaching and statistics to applied scientists, the authors provide a beginner’s guide to . To avoid the difficulty of teaching and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install , 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 .

[2009, book| url]

Gries, S. T. (2009). Quantitative corpus linguistics with R: A practical introduction. New York, NY: Routledge.

This book demonstrates how to use R for corpus linguistic analyses. Computational and corpus linguists doing corpus work will find that R provides an enormous range of functions that currently require several programs to achieve - searching and processing corpora, arranging and outputting the results of corpus searches, statistical evaluation, and graphing.

[2009, book| url]

Sheather, S. J. (2009). A Modern Approach to Regression with R. New York, NY: Springer.

A Modern Approach to Regression with R focuses on tools and techniques for building regression models using real-world data and assessing their validity. 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. You will also find SAS-code and STATA-code to produce the equivalent output on the book website. Primers containing expanded explanations of R, SAS and STATA and their use in this book are also available on the book website. 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.