XML and Web Technologies for Data Sciences with R, Deborah Nolan; Duncan Temple Lang
Автор: Mikhail V. Batsyn; Valery A. Kalyagin; Panos M. Pa Название: Models, Algorithms and Technologies for Network Analysis ISBN: 3319097571 ISBN-13(EAN): 9783319097572 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume compiles the major results of conference participants from the "Third International Conference in Network Analysis" held at the Higher School of Economics, Nizhny Novgorod in May 2013, with the aim to initiate further joint research among different groups.
Описание: This textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R. It is developed for undergraduate students in agriculture, nursing, biology and biomedical research.
Автор: Donatella Vicari; Akinori Okada; Giancarlo Ragozin Название: Analysis and Modeling of Complex Data in Behavioral and Social Sciences ISBN: 3319066919 ISBN-13(EAN): 9783319066912 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Time-frequency Filtering for Seismic Waves Clustering.- Modeling Longitudinal Data by Latent Markov Models with Application to Educational and Psychological Measurement.- Clustering of Stratified Aggregated Data using the Aggregate Association Index: Analysis of New Zealand Voter Turnout (1893 - 1919).- Estimating a Rasch Model via Fuzzy Empirical Probability Functions.- Scale Reliability Evaluation for a-priori Clustered Data.- An Evaluation of Performance of Territorial Services Center (TSC) by a Nonparametric Combination Ranking Method. The IQuEL Italian Project.- A New Index for the Comparison of Different Measurement Scales.- Asymmetries in Organizational Structures.- A Generalized Additive Model for Binary Rare Events Data: an Application to Credit Defaults.- The Meaning of forma in Thomas Aquinas. Hierarchical Clustering from the Index Thomisticus Treebank.- The Estimation of the Parameters in Multi-Criteria Classification Problem: the Case of the Electre Tri Method.- Dynamic Clustering of Financial Assets.- A Comparison of metrics for the Assessment of Relational Similarities in Affiliation Networks.- Influence Diagnostics for Meta-Analysis of Individual Patient Data using Generalized Linear Mixed Models.- Social Networks as Symbolic Data.- Statistical Assessment for Risk Prediction of Endoleak Formation after TEVAR Based on Linear Discriminant Analysis.- Fuzzy c-means for Web Mining: The Italian Tourist Forum Case.- On Joint Dimension Reduction and Clustering of Categorical Data.- A SVM Applied Text Categorization of Academia-Industry Collaborative Research and Development Documents on the Web.- Dynamic Customer Satisfaction and Measure of Trajectories: a Banking Case.- The Analysis of Partnership Networks in Social Planning Processes.- Evaluating the Effect of New Brand by Asymmetric Multidimensional Scaling.- Statistical Characterization of the Virtual Water Trade Network.- A Pre-Specified Blockmodeling to Analyze Structural Dynamics in Innovation Networks.- The RCI as a Measure of Monotonic Dependence.- A Value Added Approach in Upper Secondary Schools of Lombardy by OECD-PISA 2009 Data.- Algorithmic-Type Imputation Techniques with Different Data Structures: Alternative Approaches in Comparison.- Changes in Japanese EFL Learners' Proficiency: An Application of Latent Rank Theory.- Robustness and Stability Analysis of Factor PD-Clustering on Large Social Data Sets.- A Box-plot and Outliers Detection Proposal for Histogram Data: New Tools for Data Stream Analysis.- Assessing Cooperation in Open Systems: an Empirical Test in Healthcare.
Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.
New to the Second Edition
The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
New chapter on simulation that includes examples of data generated from complex models and distributions
A detailed discussion of the philosophy and use of the knitr and markdown packages for R
New packages that extend the functionality of R and facilitate sophisticated analyses
Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots
Easily Find Your Desired Task
Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.
Автор: Mikhail V. Batsyn; Valery A. Kalyagin; Panos M. Pa Название: Models, Algorithms and Technologies for Network Analysis ISBN: 3319343521 ISBN-13(EAN): 9783319343525 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume compiles the major results of conference participants from the "Third International Conference in Network Analysis" held at the Higher School of Economics, Nizhny Novgorod in May 2013, with the aim to initiate further joint research among different groups.
Автор: Antonella Contin; Paolo Paolini; Rossella Salerno Название: Innovative Technologies in Urban Mapping ISBN: 3319037978 ISBN-13(EAN): 9783319037974 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Innovative Technologies in Urban Mapping
Автор: Van Houwelingen Название: Dynamic Prediction in Clinical Survival Analysis ISBN: 1439835330 ISBN-13(EAN): 9781439835333 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models.
Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts:
Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model
Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated
Part III is dedicated to the use of time-dependent information in dynamic prediction
Part IV explores dynamic prediction models for survival data using genomic data
Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.
Описание: This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers onhow R is used for nonparametric data analysis in the biological sciences:To introduce when nonparametricapproaches to data analysis are appropriateTo introduce the leadingnonparametric tests commonly used in biostatistics and how R is used togenerate appropriate statistics for each testTo introduce common figurestypically associated with nonparametric data analysis and how R is used togenerate appropriate figures in support of each data setThe book focuses on how R is used todistinguish between data that could be classified as nonparametric as opposedto data that could be classified as parametric, with both approaches to data classification covered extensively.Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.This supplemental text is intended for:Upper-level undergraduate and graduate students majoring in the biological sciences, specifically those in agriculture, biology, and health science - both students in lecture-type courses and also those engaged in research projects, such as a master's thesis or a doctoral dissertationAnd biological researchers at the professional level without a nonparametric statistics background but who regularly work with data more suitable to a nonparametric approach to data analysis
Описание: This book is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical biological and life science problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you.Excel is an effective learning tool for quantitative analyses in biological and life sciences courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Biological and Life Sciences Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel 2016 to statistical techniques necessary in their courses and work.Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand biological and life science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.
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