Articles by "R Language"

Showing posts with label R Language. Show all posts

Kandethody M. Ramachandran, Chris P. Tsokos ... 704 pages - Publisher: Academic Press; 3rd edition (June, 2020) ... Language: English - ISBN-10: 0128178159 - ISBN-13: 978-0128178157.

Mathematical Statistics with Applications in R, Third Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods, such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem-solving in a logical manner. Step-by-step procedure to solve real problems make the topics very accessible.

Sorin Draghici ... 1036 pages - Publisher: Chapman and Hall/CRC; 2nd edition (April, 2016) ... Language: English - ASIN: B00O5D331Q by Amazon.

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems.

New to the Second Edition: Completely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying CD-ROM. With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.

Farhad Hosseinzadeh Lotfi, Ali Ebrahimnejad, Mohsen Vaez-Ghasemi, Zohreh Moghaddas ... 236 pages - Publisher: Springer; (July, 2019) ... Language: English - ASIN: B07VPCDJL5 by Amazon.

This book introduces readers to the use of R codes for optimization problems. First, it provides the necessary background to understand data envelopment analysis (DEA), with a special emphasis on fuzzy DEA. It then describes DEA models, including fuzzy DEA models, and shows how to use them to solve optimization problems with R. Further, it discusses the main advantages of R in optimization problems, and provides R codes based on real-world data sets throughout. Offering a comprehensive review of DEA and fuzzy DEA models and the corresponding R codes, this practice-oriented reference guide is intended for masters and Ph.D. students in various disciplines, as well as practitioners and researchers.

Norman Matloff ... 444 pages - Publisher: Routledge; (June, 2019) ... Language: English - ISBN-10: 1138393290 - ISBN-13: 978-1138393295.

Probability and Statistics for Data Science: Math + R + Data covers "math stat"―distributions, expected value, estimation etc.―but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming.

Thomas Mailund ... 386 pages - Publisher: Apress; (March, 2017) ... Language: English - ASIN: B06XHZVBF1 by Amazon.

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.
Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. 

What You Will Learn: Perform data science and analytics using statistics and the R programming language + Visualize and explore data, including working with large data sets found in big data + Build an R package + Test and check your code + Practice version control + Profile and optimize your code.

Galit Shmueli, Kenneth C. Lichtendahl Jr. ... 232 pages - Publisher: Axelrod Schnall Publishers; 2nd edition (July, 2016) ... Language: English - ISBN-10: 0997847913 - ISBN-13: 978-0997847918. 

Practical Time Series Forecasting with R: A Hands-On Guide, 2nd Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source R software to develop effective forecasting solutions that extract business value from time-series data.

Featuring improved organization and new material, the Second Edition also includes: Popular forecasting methods including smoothing algorithms, regression models, and neural networks + A practical approach to evaluating the performance of forecasting solutions + A business-analytics exposition focused on linking time-series forecasting to business goals + Guided cases for integrating the acquired knowledge using real data + End-of-chapter problems to facilitate active learning + A companion site with data sets, R code, learning resources, and instructor materials (solutions to exercises, case studies)Globally-available textbook, available in both softcover and Kindle formats

Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition is the perfect textbook for upper-undergraduate, graduate and MBA-level courses as well as professional programs in data science and business analytics. The book is also designed for practitioners in the fields of operations research, supply chain management, marketing, economics, finance and management.

Christian Ritz, Signe Marie Jensen, Daniel Gerhard, Jens Carl Streibig ... 226 pages - Publisher: Chapman and Hall/CRC; (July, 2019) ... Language: English - ISBN-10: 1138034312 - ISBN-13: 978-1138034310

Nowadays the term dose-response is used in many different contexts and many different scientific disciplines including agriculture, biochemistry, chemistry, environmental sciences, genetics, pharmacology, plant sciences, toxicology, and zoology. In the 1940 and 1950s, dose-response analysis was intimately linked to evaluation of toxicity in terms of binary responses, such as immobility and mortality, with a limited number of doses of a toxic compound being compared to a control group (dose 0). Later, dose-response analysis has been extended to other types of data and to more complex experimental designs. Moreover, estimation of model parameters has undergone a dramatic change, from struggling with cumbersome manual operations and transformations with pen and paper to rapid calculations on any laptop. Advances in statistical software have fueled this development.

Key Features: Provides a practical and comprehensive overview of dose-response analysis. + Includes numerous real data examples to illustrate the methodology. + R code is integrated into the text to give guidance on applying the methods. + Written with minimal mathematics to be suitable for practitioners. + Includes code and datasets on the book’s GitHub: https://github.com/DoseResponse. This book focuses on estimation and interpretation of entirely parametric nonlinear dose-response models using the powerful statistical environment R. Specifically, this book introduces dose-response analysis of continuous, binomial, count, multinomial, and event-time dose-response data. The statistical models used are partly special cases, partly extensions of nonlinear regression models, generalized linear and nonlinear regression models, and nonlinear mixed-effects models (for hierarchical dose-response data). Both simple and complex dose-response experiments will be analyzed.

John Fox, Sanford Weisberg ... 608 pages - Publisher: SAGE Publications; 3rd edition (October, 2018) ... Language: English - ISBN-10: 1544336470 - ISBN-13: 978-1544336473 ...

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the carand effects packages for R for this new edition, and include coverage of RStudio and R Markdown.

Nataraj Dasgupta ... 412 pages - Publisher: Packt Publishing; (January, 2018) ... Language: English - ISBN-10: 9781783554393 - ISBN-13: 978-1783554393 ...

Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book.

Giuseppe Ciaburro ... 422 pages - Publisher: Packt Publishing; (January, 2018) ... Language: English - ISBN-10: 178862730X - ISBN-13: 978-1788627306 ...

Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are - supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process - loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.

Hossein Riazoshams, Habshah Midi, Gebrenegus Ghilagaber ... 264 pages - Publisher: Wiley; 1st edition (June, 2018) ... Language: English - ASIN: B07DP92FBT by Amazon ...

The first book to discuss robust aspects of nonlinear regression—with applications using R software: Robust Nonlinear Regression: with Applications using R covers a variety of theories and applications of nonlinear robust regression. It discusses both parts of the classic and robust aspects of nonlinear regression and focuses on outlier effects. It develops new methods in robust nonlinear regression and implements a set of objects and functions in S-language under SPLUS and R software. The software covers a wide range of robust nonlinear fitting and inferences, and is designed to provide facilities for computer users to define their own nonlinear models as an object, and fit models using classic and robust methods as well as detect outliers. The implemented objects and functions can be applied by practitioners as well as researchers. The book offers comprehensive coverage of the subject in 9 chapters: Theories of Nonlinear Regression and Inference; Introduction to R; Optimization; Theories of Robust Nonlinear Methods; Robust and Classical Nonlinear Regression with Autocorrelated and Heteroscedastic errors; Outlier Detection; R Packages in Nonlinear Regression; A New R Package in Robust Nonlinear Regression; and Object Sets. The first comprehensive coverage of this field covers a variety of both theoretical and applied topics surrounding robust nonlinear regression
Addresses some commonly mishandled aspects of modeling. R packages for both classical and robust nonlinear regression are presented in detail in the book and on an accompanying websiteRobust Nonlinear Regression: with Applications using R is an ideal text for statisticians, biostatisticians, and statistical consultants, as well as advanced level students of statistics.

Conrad Carlberg ... 272 pages - Publisher: Que Publishing; 1st edition (November, 2016) ... Language: English - ISBN-10: 0789757850 - ISBN-13: 978-0789757852 ...

Microsoft Excel can perform many statistical analyses, but thousands of business users and analysts are now reaching its limits. R, in contrast, can perform virtually any imaginable analysis—if you can get over its learning curve. In R for Microsoft® Excel Users, Conrad Carlberg shows exactly how to get the most from both programs. Drawing on his immense experience helping organizations apply statistical methods, Carlberg reviews how to perform key tasks in Excel, and then guides you through reaching the same outcome in R—including which packages to install and how to access them. Carlberg offers expert advice on when and how to use Excel, when and how to use R instead, and the strengths and weaknesses of each tool. Writing in clear, understandable English, Carlberg combines essential statistical theory with hands-on examples reflecting real-world challenges. By the time you’ve finished, you’ll be comfortable using R to solve a wide spectrum of problems—including many you just couldn’t handle with Excel. Contents: • Smoothly transition to R and its radically different user interface • Leverage the R community’s immense library of packages • Efficiently move data between Excel and R • Use R’s DescTools for descriptive statistics, including bivariate analyses • Perform regression analysis and statistical inference in R and Excel • Analyze variance and covariance, including single-factor and factorial ANOVA • Use R’s mlogit package and glm function for Solver-style logistic regression • Analyze time series and principal components with R and Excel.

Giuseppe Ciaburro, Balaji Venkateswaran ... 270 pages - Publisher: Packt Publishing; (September, 2017) ... Language: English - ISBN-10: 1788397878 - ISBN-13: 978-1788397872 ...

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.

David E. Hiebeler ... 233 pages - Publisher: Chapman and Hall/CRC; (June, 2015) ... Language: English - ISBN-10: 1466568380 - ISBN-13: 978-1466568389 ... 

The First Book to Explain How a User of R or MATLAB Can Benefit from the Other: In today’s increasingly interdisciplinary world, R and MATLAB® users from different backgrounds must often work together and share code. R and MATLAB® is designed for users who already know R or MATLAB and now need to learn the other platform. The book makes the transition from one platform to the other as quick and painless as possible. Enables R and MATLAB Users to Easily Collaborate and Share Code: The author covers essential tasks, such as working with matrices and vectors, writing functions and other programming concepts, graphics, numerical computing, and file input/output. He highlights important differences between the two platforms and explores common mistakes that are easy to make when transitioning from one platform to the other.

R v3.5.1 [Size: 79 MB] ... R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

Jared P. Lander ... 560 pages - Publisher: Addison-Wesley Professional; 2nd edition (June, 2017) ... Language: English - ISBN-10: 013454692X - ISBN-13: 978-0134546926 ...

Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes: Explore R, RStudio, and R packages * Use R for math: variable types, vectors, calling functions, and more * Exploit data structures, including data.frames, matrices, and lists * Read many different types of data * Create attractive, intuitive statistical graphics * Write user-defined functions * Control program flow with if, ifelse, and complex checks * Improve program efficiency with group manipulations * Combine and reshape multiple datasets * Manipulate strings using R’s facilities and regular expressions * Create normal, binomial, and Poisson probability distributions * Build linear, generalized linear, and nonlinear models *Program basic statistics: mean, standard deviation, and t-tests * Train machine learning models * Assess the quality of models and variable selection * Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods * Analyze univariate and multivariate time series data * Group data via K-means and hierarchical clustering * Prepare reports, slideshows, and web pages with knitr * Display interactive data with RMarkdown and htmlwidgets * Implement dashboards with Shiny * Build reusable R packages with devtools and Rcpp.

Rafael A. Irizarry, Michael I. Love ... 376 pages - Publisher: Chapman and Hall/CRC; 1st edition (August, 2016) ... Language: English - ISBN-10: 1498775675 - ISBN-13: 978-1498775670 ...

This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.

Mike McGrath ... 192 pages - Publisher: In Easy Steps Limited; (March, 2018) ... Language: English - ISBN-10: 1840787953 - ISBN-13: 978-1840787955 ...

The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. R for Data Analysis in easy steps has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. R for Data Analysis in easy steps begins by explaining core programming principles of the R programming language, which stores data in “vectors” from which simple graphs can be plotted. Next, the book describes how to create “matrices” to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create “data frames” from imported data sets, and how to employ the “Grammar of Graphics” to produce advanced visualizations that can best illustrate useful insights from your data. R for Data Analysis in easy steps contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding. By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming.

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