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Learn R As a Language

Learn R As a Language

Learning a computer language like R can be either frustrating fun or boring. Having fun requires challenges that wake up the learner’s curiosity but also provide an emotional reward on overcoming them. This book is designed so that it includes smaller and bigger challenges in what I call playgrounds in the hope that all readers will enjoy their path to R fluency. Fluency in the use of a language is a skill that is acquired through practice and exploration. Although rarely mentioned separately fluency in a computer programming language involves both writing and reading. The parallels between natural and computer languages are many but differences are also important. For students and professionals in the biological sciences humanities and many applied fields recognizing the parallels between R and natural languages should help them feel at home with R. The approach I use is similar to that of a travel guide encouraging exploration and describing the available alternatives and how to reach them. The intention is to guide the reader through the R landscape of 2020 and beyond. Features R as it is currently used Few prescriptive rules—mostly the author’s preferences together with alternatives Explanation of the R grammar emphasizing the R way of doing things Tutoring for programming in the small using scripts The grammar of graphics and the grammar of data described as grammars Examples of data exchange between R and the foreign world using common file formats Coaching for becoming an independent R user capable of both writing original code and solving future challenges What makes this book different from others: Tries to break the ice and help readers from all disciplines feel at home with R Does not make assumptions about what the reader will use R for Attempts to do only one thing well: guide readers into becoming fluent in the R language Pedro J. Aphalo is a PhD graduate from the University of Edinburgh and is currently a lecturer at the University of Helsinki. A plant biologist and agriculture scientist with a passion for data electronics computers and photography in addition to plants Dr. Aphalo has been a user of R for 25 years. He first organized an R course for MSc students 18 years ago and is the author of 13 R packages currently in CRAN. | Learn R As a Language

GBP 56.99
1

Advanced R Second Edition

Advanced R Second Edition

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional object-oriented and metaprogramming); and powerful techniques for debugging and optimisingyour code. By reading this book you will learn: The difference between an object and its name and why the distinction is important The important vector data structures how they fit together and how you can pull them apart using subsetting The fine details of functions and environments The condition system which powers messages warnings and errors The powerful functional programming paradigm which can replace many for loops The three most important OO systems: S3 S4 and R6 The tidy eval toolkit for metaprogramming which allows you to manipulate code and control evaluation Effective debugging techniques that you can deploy regardless of how your code is run How to find and remove performance bottlenecks The second edition is a comprehensive update: New foundational chapters: Names and values Control flow and Conditions comprehensive coverage of object oriented programming with chapters on S3 S4 R6 and how to choose between them Much deeper coverage of metaprogramming including the new tidy evaluation framework use of new package like rlang (http://rlang. r-lib. org) which provides a clean interface to low-level operations and purr (http://purrr. tidyverse. org/) for functional programming Use of color in code chunks and figuresHadley Wickham is Chief Scientist at RStudio an Adjunct Professor at Stanford University and the University of Auckland and a member of the R Foundation. He is the lead developer of the tidyverse a collection of R packages including ggplot2 and dplyr designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund) R Packages and ggplot2: Elegant Graphics for Data Analysis. | Advanced R Second Edition

GBP 48.99
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Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra interpolation numerical integration optimization and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R along with examples to demonstrate the function and its use. Computational Methods for Numerical Analysis with R is intended for those who already know R but are interested in learning more about how the underlying algorithms work. As such it is suitable for statisticians economists and engineers and others with a computational and numerical background.

GBP 48.99
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An R Companion to Linear Statistical Models

An R Companion to Linear Statistical Models

Focusing on user-developed programming An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R the book begins with a self-contained introduction to R that lays the foundation for later chapters. This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.

GBP 74.99
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Rasch Measurement Theory Analysis in R

Rasch Measurement Theory Analysis in R

Rasch Measurement Theory Analysis in R provides researchers and practitioners with a step-by-step guide for conducting Rasch measurement theory analyses using R. It includes theoretical introductions to major Rasch measurement principles and techniques demonstrations of analyses using several R packages that contain Rasch measurement functions and sample interpretations of results. Features: Accessible to users with relatively little experience with R programming Reproducible data analysis examples that can be modified to accommodate users’ own data Accompanying e-book website with links to additional resources and R code updates as needed Features dichotomous and polytomous (rating scale) Rasch models that can be applied to data from a wide range of disciplines This book is designed for graduate students researchers and practitioners across the social health and behavioral sciences who have a basic familiarity with Rasch measurement theory and with R. Readers will learn how to use existing R packages to conduct a variety of analyses related to Rasch measurement theory including evaluating data for adherence to measurement requirements applying the dichotomous Rating Scale Partial Credit and Many-Facet Rasch models examining data for evidence of differential item functioning and considering potential interpretations of results from such analyses.

GBP 56.99
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Using the Rorschach Performance Assessment System (R-PAS )

Using the Rorschach Performance Assessment System (R-PAS )

From codevelopers of the Rorschach Performance Assessment System (R-PAS) this essential casebook illustrates the utility of R-PAS for addressing a wide range of common referral questions with adults children and adolescents. Compelling case examples from respected experts cover clinical issues (such as assessing psychosis personality disorders and suicidality); forensic issues (such as insanity and violence risk assessments child custody proceedings and domestic violence); and use in neuropsychological educational and other settings. Each tightly edited chapter details R-PAS administration coding and interpretation. Designed to replace the widely used Comprehensive System developed by John Exner R-PAS has a stronger empirical foundation is accurately normed for international use is easier to learn and use and reduces ambiguities in administration and coding among other improvements. Visit www. r-pas. org for more information. | Using the Rorschach Performance Assessment System® (R-PAS®)

GBP 47.99
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Optimization Modelling Using R

Optimization Modelling Using R

This book covers using R for doing optimization a key area of operations research which has been applied to virtually every industry. The focus is on linear and mixed integer optimization. It uses an algebraic modeling approach for creating formulations that pairs naturally with an algebraic implementation in R. With the rapid rise of interest in data analytics a data analytics platform is key. Working technology and business professionals need an awareness of the tools and language of data analysis. R reduces the barrier to entry for people to start using data analytics tools. Philosophically the book emphasizes creating formulations before going intoimplementation. Algebraic representation allows for clear understanding and generalizationof large applications and writing formulations is necessary to explain and convey the modeling decisions made. Appendix A introduces R. Mathematics is used at the level of subscripts and summations Refreshers are provided in Appendix B. This book: • Provides and explains code so examples are relatively clear and self-contained. • Emphasizes creating algebraic formulations before implementing. • Focuses on application rather than algorithmic details. • Embodies the philosophy of reproducible research. • Uses open-source tools to ensure access to powerful optimization tools. • Promotes open-source: all materials are available on the author’s github repository. • Demonstrates common debugging practices with a troubleshooting emphasis specific to optimization modeling using R. • Provides code readers can adapt to their own applications. This book can be used for graduate and undergraduate courses for students without a background in optimization and with varying mathematical backgrounds. | Optimization Modelling Using R

GBP 82.99
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Exploratory Data Analysis Using R

Exploratory Data Analysis Using R

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of interesting – good bad and ugly – features that can be found in data and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data exploratory analysis and R as well as graphics in R. It then explores working with external data linear regression models and crafting data stories. The second part of the book focuses on developing R programs including good programming practices and examples working with text data and general predictive models. The book ends with a chapter on keeping it all together that includes managing the R installation managing files documenting and an introduction to reproducible computing. The book is designed for both advanced undergraduate entry-level graduate students and working professionals with little to no prior exposure to data analysis modeling statistics or programming. it keeps the treatment relatively non-mathematical even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters and an instructor's solution manual is available. About the Author:Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera a property insurance company in Fairfield California and he has previously held similar positions in a variety of application areas including software development drug safety data analysis and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering the Sciences and Medicine (Oxford University Press 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

GBP 44.99
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Geocomputation with R

Geocomputation with R

Geocomputation with R is for people who want to analyze visualize and model geographic data with open source software. It is based on R a statistical programming language that has powerful data processing visualization and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data including those with scientific societal and environmental implications. This book will interest people from many backgrounds especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations aimed at getting you up-to-speed with geographic data in R (II) extensions which covers advanced techniques and (III) applications to real-world problems. The chapters cover progressively more advanced topics with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping) bridges to GIS sharing reproducible code and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems including representing and modeling transport systems finding optimal locations for stores or services and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr. github. io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds where he has taught R for geographic research over many years with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena where he develops and teaches a range of geographic methods with a focus on ecological modeling statistical geocomputing and predictive mapping. All three are active developers and work on a number of R packages including stplanr sabre and RQGIS.

GBP 44.99
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Applied Meta-Analysis with R and Stata

Applied Meta-Analysis with R and Stata

Review of the First Edition: The authors strive to reduce theory to a minimum which makes it a self-learning text that is comprehensible for biologists physicians etc. who lack an advanced mathematics background. Unlike in many other textbooks R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses graphics and simulations directly relating to meta-analysis… A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. —Journal of Applied Statistics Statistical Meta-Analysis with R and Stata Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What’s New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA meta-regression MA with rare-event and MA-IPD vs MA-SS Adds five new chapters on multivariate MA publication bias missing data in MA MA in evaluating diagnostic accuracy and network MA Suitable as a graduate-level text for a meta-data analysis course the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health medical research governmental agencies and the pharmaceutical industry. | Applied Meta-Analysis with R and Stata

GBP 44.99
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Applications of Regression for Categorical Outcomes Using R

Applications of Regression for Categorical Outcomes Using R

This book covers the main models within the GLM (i. e. logistic Poisson negative binomial ordinal and multinomial). For each model estimations interpretations model fit diagnostics and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata SPSS and SAS to using R and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge and for Quantitative social scientists due to it’s ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy calculator. Our programs will enable users to derive quantities that they can use in their work Timely- many in the social sciences are currently transitioning to R or are learning it now. Our book will be a useful resource Versatile- we will write functions into an R package that can be applied to all of the regression models we will cover in the book Aesthetically pleasing- one advantage of R relative to other software packages is that graphs are fully customizable. We will leverage this feature to yield high-end graphical displays of results Affordability- R is free. R packages are free. There is no need to purchase site licenses or updates.

GBP 59.99
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Data Mining with R Learning with Case Studies Second Edition

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R: Learning with Case Studies Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition this new edition is divided into two parts. The first part will feature introductory material including a new chapter that provides an introduction to data mining to complement the already existing introduction to R. The second part includes case studies and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies and they facilitate the do-it-yourself approach followed in the book. Designed for users of data analysis tools as well as researchers and developers the book should be useful for anyone interested in entering the world of R and data mining. About the AuthorLuís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA. | Data Mining with R Learning with Case Studies Second Edition

GBP 44.99
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Stochastic Processes with R An Introduction

Stochastic Processes with R An Introduction

Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. The light yet detailed text provides a solid foundation that is an ideal companion for undergraduate statistics students looking to familiarize themselves with stochastic processes before going on to more advanced courses. Key Features Provides complete R codes for all simulations and calculations Substantial scientific or popular applications of each process with occasional statistical analysis Helpful definitions and examples are provided for each process End of chapter exercises cover theoretical applications and practice calculations | Stochastic Processes with R An Introduction

GBP 82.99
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Doing Meta-Analysis with R A Hands-On Guide

Doing Meta-Analysis with R A Hands-On Guide

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered including calculation and pooling of outcome measures forest plots heterogeneity diagnostics subgroup analyses meta-regression methods to control for publication bias risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis multi-three-level meta-analyses Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package dmetar is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level making the book widely accessible. Features• Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data including exercises• Describes statistical concepts clearly and concisely before applying them in R• Includes step-by-step guidance through the coding required to perform meta-analyses and a companion R package for the book | Doing Meta-Analysis with R A Hands-On Guide

GBP 74.99
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Surveying with Geomatics and R

Surveying with Geomatics and R

Surveying with Geomatics and R This book explains basic concepts of surveying science and techniques with geomatics using R software and R packages. It engages students in learning about surveying through real field examples and using differing degrees of complexity while exploring surveying problems based on field observations and advanced geospatial technology. It includes a wide range of case studies as hands-on and self-paced tutorials along with detailed computer programming routines that are linked to the theories and applications explained in each chapter. This innovative textbook also teaches how to explore other possibilities of using geomatics in geocomputation remote sensing geography and cartography courses focused on surveying tasks. Features include: Provides modern surveying practices with free software algorithm and R toolset for active learning Includes case studies from different geographical areas using arbitrary and international cartographic reference systems Enables and demonstrates the integration of traditional geomatics with modern geospatial big data technologies Explains data standards equipment used possible analyses and the importance of error evaluation for scientific surveying Discusses different scales of landscapes and brings together the experiences of leading experts in the field

GBP 82.99
1

Statistical Computing with R Second Edition

Statistical Computing with R Second Edition

Praise for the First Edition: . the book serves as an excellent tutorial on the R language providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation. – Tzvetan Semerdjiev Zentralblatt Math Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational graphical and numerical approaches to solving statistical problems. Like its bestselling predecessor Statistical Computing with R Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years. Features Provides an overview of computational statistics and an introduction to the R computing environment. Focuses on implementation rather than theory. Explores key topics in statistical computing including Monte Carlo methods in inference bootstrap and jackknife permutation tests Markov chain Monte Carlo (MCMC) methods and density estimation. Includes new sections exercises and applications as well as new chapters on resampling methods and programming topics. Includes coverage of recent advances including R Studio the tidyverse knitr and ggplot2 Accompanied by online supplements available on GitHub including R code for all the exercises as well as tutorials and extended examples on selected topics. Suitable for an introductory course in computational statistics or for self-study Statistical Computing with R Second Edition provides a balanced accessible introduction to computational statistics and statistical computing. About the Author Maria Rizzo is Professor in the Department of Mathematics and Statistics at Bowling Green State University in Bowling Green Ohio where she teaches statistics actuarial science computational statistics statistical programming and data science. Prior to joining the faculty at BGSU in 2006 she was Assistant Professor in the Department of Mathematics at Ohio University in Athens Ohio. Her main research area is energy statistics and distance correlation. She is the software developer and maintainer of the energy package for R. She also enjoys writing books including a forthcoming joint research monograph on energy statistics.

GBP 66.99
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R for Political Data Science A Practical Guide

R for Political Data Science A Practical Guide

R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first on the use of R is for those users who are learning R or are migrating from another software. The second section on econometric models covers OLS binary and survival models panel data and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation fuzzy merge of large datasets web mining quantitative text analysis network analysis mapping spatial cluster analysis and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package including all data As the title suggests this book is highly applied in nature and is designed as a toolbox for the reader. It can be used in methods and data science courses at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD political consultants or a public official all of whom need to transform their datasets into substantive and easily interpretable conclusions. | R for Political Data Science A Practical Guide

GBP 44.99
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A Criminologist's Guide to R Crime by the Numbers

A Criminologist's Guide to R Crime by the Numbers

A Criminologist's Guide to R: Crime by the Numbers introduces the programming language R and covers the necessary skills to conduct quantitative research in criminology. By the end of this book a person without any prior programming experience can take raw crime data be able to clean it visualize the data present it using R Markdown and change it to a format ready for analysis. A Criminologist's Guide to R focuses on skills specifically for criminology such as spatial joins mapping and scraping data from PDFs however any social scientist looking for an introduction to R for data analysis will find this useful. Key Features: Introduction to RStudio including how to change user preference settings. Basic data exploration and cleaning – subsetting loading data regular expressions aggregating data. Graphing with ggplot2. How to make maps (hotspot maps choropleth maps interactive maps). Webscraping and PDF scraping. Project management – how to prepare for a project how to decide which projects to do best ways to collaborate with people how to store your code (using git) and how to test your code. | A Criminologist's Guide to R Crime by the Numbers

GBP 74.99
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Reproducible Research with R and RStudio

Reproducible Research with R and RStudio

Praise for previous editions:Gandrud has written a great outline of how a fully reproducible research project should look from start to finish with brief explanations of each tool that he uses along the way… Advanced undergraduate students in mathematics statistics and similar fields as well as students just beginning their graduate studies would benefit the most from reading this book. Many more experienced R users or second-year graduate students might find themselves thinking ‘I wish I’d read this book at the start of my studies when I was first learning R!’…This book could be used as the main text for a class on reproducible research … (The American Statistician) Reproducible Research with R and R Studio Third 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. Supplementary materials and example are available on the author’s website. New to the Third Edition Updated package recommendations examples URLs and removed technologies no longer in regular use. More advanced R Markdown (and less LaTeX) in discussions of markup languages and examples. Stronger focus on reproducible working directory tools. Updated discussion of cloud storage services and persistent reproducible material citation. Added discussion of Jupyter notebooks and reproducible practices in industry. Examples of data manipulation with Tidyverse tibbles (in addition to standard data frames) and pivot_longer() and pivot_wider() functions for pivoting data. Features Incorporates the most important advances that have been developed since the editions were published Describes a complete reproducible research workflow from data gathering to the presentation of results Shows how to automatically generate tables and figures using R Includes instructions on formatting a presentation document via markup languages Discusses cloud storage and versioning services particularly Github Explains how to use Unix-like shell programs for working with large research projects | Reproducible Research with R and RStudio

GBP 56.99
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Clinical Trial Data Analysis Using R and SAS

Clinical Trial Data Analysis Using R and SAS

Review of the First EditionThe goal of this book as stated by the authors is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall this book achieves the goal successfully and does a nice job. I would highly recommend it …The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods. —Journal of Statistical SoftwareClinical Trial Data Analysis Using R and SAS Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data. What’s New in the Second EditionAdds SAS programs along with the R programs for clinical trial data analysis. Updates all the statistical analysis with updated R packages. Includes correlated data analysis with multivariate analysis of variance. Applies R and SAS to clinical trial data from hypertension duodenal ulcer beta blockers familial andenomatous polyposis and breast cancer trials. Covers the biostatistical aspects of various clinical trials including treatment comparisons time-to-event endpoints longitudinal clinical trials and bioequivalence trials.

GBP 44.99
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Linear Regression Models Applications in R

Linear Regression Models Applications in R

Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment. After furnishing some background material the author explains how to estimate simple and multiple LRMs in R including how to interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model adjusting for measurement error understanding the effects of influential observations and using the model with multilevel data. The concluding chapter presents an alternative model—logistic regression—designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data and provides simulations in R to test model assumptions. Features Furnishes a thorough introduction and detailed information about the linear regression model including how to understand and interpret its results test assumptions and adapt the model when assumptions are not satisfied. Uses numerous graphs in R to illustrate the model’s results assumptions and other features. Does not assume a background in calculus or linear algebra rather an introductory statistics course and familiarity with elementary algebra are sufficient. Provides many examples using real-world datasets relevant to various academic disciplines. Fully integrates the R software environment in its numerous examples. The book is aimed primarily at advanced undergraduate and graduate students in social behavioral health sciences and related disciplines taking a first course in linear regression. It could also be used for self-study and would make an excellent reference for any researcher in these fields. The R code and detailed examples provided throughout the book equip the reader with an excellent set of tools for conducting research on numerous social and behavioral phenomena. John P. Hoffmann is a professor of sociology at Brigham Young University where he teaches research methods and applied statistics courses and conducts research on substance use and criminal behavior. | Linear Regression Models Applications in R

GBP 66.99
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ANOVA and Mixed Models A Short Introduction Using R

ANOVA and Mixed Models A Short Introduction Using R

ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and researcher a compact introduction to the analysis of data from the most popular experimental designs. Based on knowledge from an introductory course on probability and statistics the theoretical foundations of the most important models are introduced. The focus is on an intuitive understanding of the theory common pitfalls in practice and the application of the methods in R. From data visualization and model fitting up to the interpretation of the corresponding output the whole workflow is presented using R. The book does not only cover standard ANOVA models but also models for more advanced designs and mixed models which are common in many practical applications. Features Accessible to readers with a basic background in probability and statistics Covers fundamental concepts of experimental design and cause-effect relationships Introduces classical ANOVA models including contrasts and multiple testing Provides an example-based introduction to mixed models Features basic concepts of split-plot and incomplete block designs R code available for all steps Supplementary website with additional resources and updates are available here. This book is primarily aimed at students researchers and practitioners from all areas who wish to analyze corresponding data with R. Readers will learn a broad array of models hand-in-hand with R including the applications of some of the most important add-on packages. | ANOVA and Mixed Models A Short Introduction Using R

GBP 49.99
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Analyzing Health Data in R for SAS Users

Analyzing Health Data in R for SAS Users

Analyzing Health Data in R for SAS Users is aimed at helping health data analysts who use SAS accomplish some of the same tasks in R. It is targeted to public health students and professionals who have a background in biostatistics and SAS software but are new to R. For professors it is useful as a textbook for a descriptive or regression modeling class as it uses a publicly-available dataset for examples and provides exercises at the end of each chapter. For students and public health professionals not only is it a gentle introduction to R but it can serve as a guide to developing the results for a research report using R software. Features:Gives examples in both SAS and RDemonstrates descriptive statistics as well as linear and logistic regressionProvides exercise questions and answers at the end of each chapterUses examples from the publicly available dataset Behavioral Risk Factor Surveillance System (BRFSS) 2014 dataGuides the reader on producing a health analysis that could be published as a research reportGives an example of hypothesis-driven data analysisProvides examples of plots with a color insert

GBP 44.99
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Linear Algebra and Its Applications with R

Linear Algebra and Its Applications with R

This book developed from the need to teach a linear algebra course to students focused on data science and bioinformatics programs. These students tend not to realize the importance of linear algebra in applied sciences since traditional linear algebra courses tend to cover mathematical contexts but not the computational aspect of linear algebra or its applications to data science and bioinformatics. The author presents the topics in a traditional course yet offers lectures as well as lab exercises on simulated and empirical data sets. This textbook provides students a theoretical basis which can then be applied to the practical R and Python problems providing the tools needed for real-world applications. Each section starts with working examples to demonstrate how tools from linear algebra can help solve problems in applied sciences. These exercises start from easy computations such as computing determinants of matrices to practical applications on simulated and empirical data sets with R so that students learn how to get started with R along with computational examples in each section and then students learn how to apply what they've learned to problems in applied sciences. This book is designed from first principles to demonstrate the importance of linear algebra through working computational examples with R and Python including tutorials on how to install R in the Appendix. If a student has never seen R they can get started without any additional help. Since Python is one of the most popular languages in data science optimization and computer science code supplements are available for students who feel more comfortable with Python. R is used primarily for computational examples to develop students’ practical computational skills. About the Author: Dr. Ruriko Yoshida is an Associate Professor of Operations Research at the Naval Postgraduate School. She received her PhD in Mathematics from the University of California Davis. Her research topics cover a wide variety of areas: applications of algebraic combinatorics to statistical problems such as statistical learning on non-Euclidean spaces sensor networks phylogenetics and phylogenomics. She teaches courses in statistics stochastic models probability and data science. | Linear Algebra and Its Applications with R

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