Stated Preference Methods Using R

Author: Hideo Aizaki
Publisher: CRC Press
ISBN: 1439890471
Format: PDF
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Stated Preference Methods Using R explains how to use stated preference (SP) methods, which are a family of survey methods, to measure people’s preferences based on decision making in hypothetical choice situations. Along with giving introductory explanations of the methods, the book collates information on existing R functions and packages as well as those prepared by the authors. It focuses on core SP methods, including contingent valuation (CV), discrete choice experiments (DCEs), and best–worst scaling (BWS). Several example data sets illustrate empirical applications of each method with R. Examples of CV draw on data from well-known environmental valuation studies, such as the Exxon Valdez oil spill in Alaska. To explain DCEs, the authors use synthetic data sets related to food marketing and environmental valuation. The examples illustrating BWS address valuing agro-environmental and food issues. All the example data sets and code are available on the authors’ website, CRAN, and R-Forge, allowing readers to easily reproduce working examples. Although the examples focus on agricultural and environmental economics, they provide beginners with a good foundation to apply SP methods in other fields. Statisticians, empirical researchers, and advanced students can use the book to conduct applied research of SP methods in economics and market research. The book is also suitable as a primary text or supplemental reading in an introductory-level, hands-on course.

Nonparametric Statistical Methods Using R

Author: John Kloke
Publisher: CRC Press
ISBN: 1498787274
Format: PDF, Docs
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A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.

Statistics in Toxicology Using R

Author: Ludwig A. Hothorn
Publisher: CRC Press
ISBN: 1498701280
Format: PDF, ePub
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The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover, recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas, Statistics in Toxicology Using R explains the statistical analysis of selected experimental data in toxicology and presents assay-specific suggestions, such as for the in vitro micronucleus assay. Mostly focusing on hypothesis testing, the book covers standardized bioassays for chemicals, drugs, and environmental pollutants. It is organized according to selected toxicological assays, including: Short-term repeated toxicity studies Long-term carcinogenicity assays Studies on reproductive toxicity Mutagenicity assays Toxicokinetic studies The book also discusses proof of safety (particularly in ecotoxicological assays), toxicogenomics, the analysis of interlaboratory studies and the modeling of dose-response relationships for risk assessment. For each toxicological problem, the author describes the statistics involved, matching data example, R code, and outcomes and their interpretation. This approach allows you to select a certain bioassay, identify the specific data structure, run the R code with the data example, understand the test outcome and interpretation, and replace the data set with your own data and run again.

Introductory Fisheries Analyses with R

Author: Derek H. Ogle
Publisher: CRC Press
ISBN: 1482235226
Format: PDF, ePub, Docs
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A How-To Guide for Conducting Common Fisheries-Related Analyses in R Introductory Fisheries Analyses with R provides detailed instructions on performing basic fisheries stock assessment analyses in the R environment. Accessible to practicing fisheries scientists as well as advanced undergraduate and graduate students, the book demonstrates the flexibility and power of R, offers insight into the reproducibility of script-based analyses, and shows how the use of R leads to more efficient and productive work in fisheries science. The first three chapters present a minimal introduction to the R environment that builds a foundation for the fisheries-specific analyses in the remainder of the book. These chapters help you become familiar with R for basic fisheries analyses and graphics. Subsequent chapters focus on methods to analyze age comparisons, age-length keys, size structure, weight-length relationships, condition, abundance (from capture-recapture and depletion data), mortality rates, individual growth, and the stock-recruit relationship. The fundamental statistical methods of linear regression, analysis of variance (ANOVA), and nonlinear regression are demonstrated within the contexts of these common fisheries analyses. For each analysis, the author completely explains the R functions and provides sufficient background information so that you can confidently implement each method. Web Resource The author’s website at http://derekogle.com/IFAR/ includes the data files and R code for each chapter, enabling you to reproduce the results in the book as well as create your own scripts. The site also offers supplemental code for more advanced analyses and practice exercises for every chapter.

Computational Statistics Handbook with MATLAB Third Edition

Author: Wendy L. Martinez
Publisher: CRC Press
ISBN: 1466592745
Format: PDF, Mobi
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A Strong Practical Focus on Applications and Algorithms Computational Statistics Handbook with MATLAB®, Third Edition covers today’s most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the implementation of the methods. New to the Third Edition This third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and multivariate adaptive regression splines. Web Resource The authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code, examples, and data sets are available online.

Data Science in R

Author: Deborah Nolan
Publisher: CRC Press
ISBN: 1482234823
Format: PDF, Mobi
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Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book’s collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data. Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers’ computational reasoning of real-world data analyses.

Analyzing Sensory Data with R

Author: Sebastien Le
Publisher: CRC Press
ISBN: 146656573X
Format: PDF, ePub
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Choose the Proper Statistical Method for Your Sensory Data Issue Analyzing Sensory Data with R gives you the foundation to analyze and interpret sensory data. The book helps you find the most appropriate statistical method to tackle your sensory data issue. Covering quantitative, qualitative, and affective approaches, the book presents the big picture of sensory evaluation. Through an integrated approach that connects the different dimensions of sensory evaluation, you’ll understand: The reasons why sensory data are collected The ways in which the data are collected and analyzed The intrinsic meaning of the data The interpretation of the data analysis results Each chapter corresponds to one main sensory topic. The chapters start with presenting the nature of the sensory evaluation and its objectives, the sensory particularities related to the sensory evaluation, details about the data set obtained, and the statistical analyses required. Using real examples, the authors then illustrate step by step how the analyses are performed in R. The chapters conclude with variants and extensions of the methods that are related to the sensory task itself, the statistical methodology, or both.

Reproducible Research with R and R Studio Second Edition

Author: Christopher Gandrud
Publisher: CRC Press
ISBN: 1498715389
Format: PDF, Mobi
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All the Tools for Gathering and Analyzing Data and Presenting Results Reproducible Research with R and RStudio, Second Edition brings together the skills and tools needed for doing and presenting computational research. Using straightforward examples, the book takes you through an entire reproducible research workflow. This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web. New to the Second Edition The rmarkdown package that allows you to create reproducible research documents in PDF, HTML, and Microsoft Word formats using the simple and intuitive Markdown syntax Improvements to RStudio’s interface and capabilities, such as its new tools for handling R Markdown documents Expanded knitr R code chunk capabilities The kable function in the knitr package and the texreg package for dynamically creating tables to present your data and statistical results An improved discussion of file organization, enabling you to take full advantage of relative file paths so that your documents are more easily reproducible across computers and systems The dplyr, magrittr, and tidyr packages for fast data manipulation Numerous modifications to R syntax in user-created packages Changes to GitHub’s and Dropbox’s interfaces Create Dynamic and Highly Reproducible Research This updated book provides all the tools to combine your research with the presentation of your findings. It saves you time searching for information so that you can spend more time actually addressing your research questions. Supplementary files used for the examples and a reproducible research project are available on the author’s website.

Flexible Regression and Smoothing

Author: Mikis D. Stasinopoulos
Publisher: CRC Press
ISBN: 1351980378
Format: PDF
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This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. GAMLSS allows any parametric distribution for the response variable and modelling all the parameters (location, scale and shape) of the distribution as linear or smooth functions of explanatory variables. This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website with code, data and additional materials.

Using R for Introductory Statistics Second Edition

Author: John Verzani
Publisher: CRC Press
ISBN: 1466590734
Format: PDF, Docs
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The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.