A User’s Guide to Network Analysis in R

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Author: Douglas Luke

Publisher: Springer

ISBN: 3319238833

Category: Mathematics

Page: 238

View: 3299

Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.

Network Analysis and Visualization in R

Quick Start Guide

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Author: Alboukadel Kassambara

Publisher: STHDA

ISBN: 1981179674

Category:

Page: 39

View: 708

Social network analysis is used to investigate the inter-relationship between entities. Examples of network structures, include: social media networks, friendship networks and collaboration networks. This book provides a quick start guide to network analysis and visualization in R. You'll learn, how to: - Create static and interactive network graphs using modern R packages. - Change the layout of network graphs. - Detect important or central entities in a network graph. - Detect community (or cluster) in a network.

Modeling Techniques in Predictive Analytics with Python and R

A Guide to Data Science

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Author: Thomas W. Miller

Publisher: FT Press

ISBN: 013389214X

Category: Computers

Page: 448

View: 7479

Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Using R for Numerical Analysis in Science and Engineering

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Author: Victor A. Bloomfield

Publisher: CRC Press

ISBN: 1439884498

Category: Mathematics

Page: 359

View: 2149

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R’s powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also: Explains how to statistically analyze and fit data to linear and nonlinear models Explores numerical differentiation, integration, and optimization Describes how to find eigenvalues and eigenfunctions Discusses interpolation and curve fitting Considers the analysis of time series Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.

Mastering Gephi Network Visualization

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Author: Ken Cherven

Publisher: Packt Publishing Ltd

ISBN: 1783987359

Category: Computers

Page: 378

View: 7394

This book is intended for anyone interested in advanced network analysis. If you wish to master the skills of analyzing and presenting network graphs effectively, then this is the book for you. No coding experience is required to use this book, although some familiarity with the Gephi user interface will be helpful.

The SAGE Handbook of Social Network Analysis

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Author: John Scott,Peter J. Carrington

Publisher: SAGE

ISBN: 1446250113

Category: Social Science

Page: 640

View: 2012

This sparkling Handbook offers an unrivalled resource for those engaged in the cutting edge field of social network analysis. Systematically, it introduces readers to the key concepts, substantive topics, central methods and prime debates. Among the specific areas covered are: Network theory Interdisciplinary applications Online networks Corporate networks Lobbying networks Deviant networks Measuring devices Key Methodologies Software applications. The result is a peerless resource for teachers and students which offers a critical survey of the origins, basic issues and major debates. The Handbook provides a one-stop guide that will be used by readers for decades to come.

Introduction to Data Analysis and Graphical Presentation in Biostatistics with R

Statistics in the Large

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Author: Thomas W. MacFarland

Publisher: Springer Science & Business Media

ISBN: 3319025325

Category: Medical

Page: 167

View: 9412

Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressing scientific issue for those engaged in biostatistics. Readers will be equipped to run analyses and make graphical presentations based on the sample dataset and their own data. The hands-on approach will benefit students and ensure the accessibility of this book for readers with a basic understanding of R. Topics include: an introduction to Biostatistics and R, data exploration, descriptive statistics and measures of central tendency, t-Test for independent samples, t-Test for matched pairs, ANOVA, correlation and linear regression, and advice for future work.

Social Network Analysis

A Handbook

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Author: John Scott

Publisher: SAGE

ISBN: 0761963383

Category: Social Science

Page: 208

View: 6970

The revised and updated edition of this bestselling text provides an accessible introduction to the theory and practice of network analysis in the social sciences. It gives a clear and authoritative guide to the general framework of network analysis, explaining the basic concepts, technical measures and reviewing the available computer programs. The book outlines both the theoretical basis of network analysis and the key techniques for using it as a research tool. Building upon definitions of points, lines and paths, John Scott demonstrates their use in clarifying such measures as density, fragmentation and centralization. He identifies the various cliques, components and circles into which networks are formed, and outlines

Users Guide to Physical Modelling and Experimentation

Experience of the HYDRALAB Network

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Author: Lynne E. Frostick,Stuart J. McLelland,T.G. Mercer

Publisher: CRC Press

ISBN: 1439870519

Category: Technology & Engineering

Page: 272

View: 5218

A Users Guide to Hydraulic Modelling and Experimentation provides a systematic, comprehensive summary of the progress made through HYDRALAB III . The book combines the expertise of many of the leading hydraulic experimentalists in Europe and identifies current best practice for carrying out state-of-the-art, modern laboratory investigations. In addition it gives an inventory and reviews recent advances in instrumentation and equipment that drive present and new developments in the subject. The Guide concentrates on four core areas – waves, breakwaters, sediments and the relatively-new (but rapidly-developing) cross-disciplinary area of hydrodynamics/ecology. Progress made through the ‘CoMIBBS’ component of HYDRALAB III provides the material for a chapter focussed on guidance, principles and practice for composite modelling. There is detailed consideration of scaling and the degree of relevance of laboratory/physical modelling approaches for specific contexts included in each of the individual chapters. The Guide includes outputs from the workshops and several of the innovative transnational access projects that have been supported within HYDRALAB III, as well as the focussed joint research activities SANDS and CoMIBBS. Its primary purpose is to serve as a shared resource to disseminate the outstanding advances achieved within HYDRALAB III but, even more than this, it is a tribute to the human and institutional collaborations that led to and sustained the research advances, the human relationships that were strengthened and initiated through joint participation in the Programme, and the training opportunities that participation provided to the many young researchers engaged in the projects.