Stated Preference Methods Using R

The book is also suitable as a primary text or supplemental reading in an introductory-level, hands-on course.

Author: Hideo Aizaki

Publisher: CRC Press

ISBN: 9781439890479

Category: Mathematics

Page: 254

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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.
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Nonparametric Statistical Methods Using R

Stated Preference Methods Using R, Hideo Aizaki, Tomoaki Nakatani, and Kazuo Sato Using R for Numerical Analysis in Science and Engineering, Victor A. Bloomfield Event History Analysis with R, Göran Broström Computational Actuarial ...

Author: John Kloke

Publisher: CRC Press

ISBN: 9781439873441

Category: Mathematics

Page: 287

View: 998

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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.
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Statistics in Toxicology Using R

Stated Preference Methods Using R, Hideo Aizaki, Tomoaki Nakatani, and Kazuo Sato Using R for Numerical Analysis in Science and Engineering, Victor A. Bloomfield Event History Analysis with R, Göran Broström Computational Actuarial ...

Author: Ludwig A. Hothorn

Publisher: CRC Press

ISBN: 9781498701280

Category: Mathematics

Page: 234

View: 682

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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.
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The Essentials of Data Science Knowledge Discovery Using R

Stated Preference Methods Using R, Hideo Aizaki, Tomoaki Nakatani, and Kazuo Sato Using R for Numerical Analysis in Science and Engineering, Victor A. Bloomfield Event History Analysis with R, Göran Broström Extending R, ...

Author: Graham J. Williams

Publisher: CRC Press

ISBN: 9781351647496

Category: Business & Economics

Page: 322

View: 183

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The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
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Using the R Commander

Spatial Microsimulation with R, Robin Lovelace and Morgane Dumont Statistics in Toxicology Using R, Ludwig A. Hothorn Stated Preference Methods Using R, Hideo Aizaki, Tomoaki Nakatani, and Kazuo Sato Using R for Numerical Analysis in ...

Author: John Fox

Publisher: CRC Press

ISBN: 9781498741934

Category: Mathematics

Page: 219

View: 168

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This book provides a general introduction to the R Commander graphical user interface (GUI) to R for readers who are unfamiliar with R. It is suitable for use as a supplementary text in a basic or intermediate-level statistics course. It is not intended to replace a basic or other statistics text but rather to complement it, although it does promote sound statistical practice in the examples. The book should also be useful to individual casual or occasional users of R for whom the standard command-line interface is an obstacle. tinyurl.com/RcmdrBook The site includes data files used in the book and an errata list. http://socserv.mcmaster.ca/jfox/Books/RCommander/Writing-Rcmdr-Plugins.pdf Writing R Commander Plug-in Packages
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Graphical Data Analysis with R

Published Titles Stated Preference Methods Using R, Hideo Aizaki, Tomoaki Nakatani, and Kazuo Sato Using R for Numerical Analysis in Science and Engineering, Victor A. Bloomfield Event History Analysis with R, Göran Broström ...

Author: Antony Unwin

Publisher: CRC Press

ISBN: 9781315360041

Category: Mathematics

Page: 310

View: 572

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See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA. Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.
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Data Science in R

Stated Preference Methods Using R, Hideo Aizaki, Tomoaki Nakatani, and Kazuo Sato Using R for Numerical Analysis in Science and Engineering, Victor A. Bloomfield Event History Analysis with R, Göran Broström Computational Actuarial ...

Author: Deborah Nolan

Publisher: CRC Press

ISBN: 9781482234824

Category: Business & Economics

Page: 539

View: 983

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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.
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Dynamic Documents with R and knitr

Published Titles Stated Preference Methods Using R, Hideo Aizaki, Tomoaki Nakatani, and Kazuo Sato Using R for Numerical Analysis in Science and Engineering, Victor A. Bloomfield Event History Analysis with R, Göran Broström ...

Author: Yihui Xie

Publisher: CRC Press

ISBN: 9781315360706

Category: Business & Economics

Page: 294

View: 488

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Quickly and Easily Write Dynamic Documents Suitable for both beginners and advanced users, Dynamic Documents with R and knitr, Second Edition makes writing statistical reports easier by integrating computing directly with reporting. Reports range from homework, projects, exams, books, blogs, and web pages to virtually any documents related to statistical graphics, computing, and data analysis. The book covers basic applications for beginners while guiding power users in understanding the extensibility of the knitr package. New to the Second Edition A new chapter that introduces R Markdown v2 Changes that reflect improvements in the knitr package New sections on generating tables, defining custom printing methods for objects in code chunks, the C/Fortran engines, the Stan engine, running engines in a persistent session, and starting a local server to serve dynamic documents Boost Your Productivity in Statistical Report Writing and Make Your Scientific Computing with R Reproducible Like its highly praised predecessor, this edition shows you how to improve your efficiency in writing reports. The book takes you from program output to publication-quality reports, helping you fine-tune every aspect of your report.
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Reproducible Research with R and R Studio

Stated Preference Methods Using R, Hideo Aizaki, Tomoaki Nakatani, and Kazuo Sato Using R for Numerical Analysis in Science and Engineering, Victor A. Bloomfeld Event History Analysis with R, Göran Broström Computational Actuarial ...

Author: Christopher Gandrud

Publisher: CRC Press

ISBN: 9781315360720

Category: Business & Economics

Page: 323

View: 206

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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.
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Introductory Fisheries Analyses with R

R. Series. Series Editors John M. Chambers Department of Statistics Stanford University Stanford, California, USA Torsten Hothorn Division of Biostatistics ... Published Titles Stated Preference Methods Using R, Hideo Aizaki, Tomoaki.

Author: Derek H. Ogle

Publisher: CRC Press

ISBN: 9781315360188

Category: Mathematics

Page: 317

View: 145

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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.
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