A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.
Praise for the first edition: “This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.” -The American Statistician This thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common machine learning topics --- including k-nearest neighbors and trees --- have also been included in this new edition. Key Features: Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type. Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate. Numerous examples illustrate the methods and their computation. R packages are available for computation and datasets. Contains two completely new chapters on big data and multivariate analysis. The book is suitable for advanced undergraduate and graduate students in statistics and data science, and students of other majors with a solid background in statistical methods including regression and ANOVA. It will also be of use to researchers working with nonparametric and rank-based methods in practice.
Hyper-Organization offers an institutional explanation for the expansion of formal organization in the contemporary era-in numbers, internal complexity, social domains, and national contexts. Much expansion is hard to justify in terms of technical production or political power, it lies in areas such as protecting the environment, promoting marginalized groups, or behaving with transparency. The authors argue that expansion is supported by widespread cultural rationalization characterized by scientism, rights and empowerment discourses, and an explosion of education. These cultural changes are transmitted through legal, accounting, and professionalization principles, driving the creation of new organizations and the elaboration of existing ones. The resulting organizations are constructed to be proper social actors, as much as functionally effective entities. They are painted as autonomous and integrated but depend heavily on external definitions to sustain this depiction. So expansion creates organizations that are, whatever their actual effectiveness, structurally arational. This book advances theories of social organization in three main ways. First, by giving an account of the expansive rise of 'organization' rooted in rapid worldwide cultural rationalization. Second, explaining the construction of contemporary organizations as purposive actors, rather than passive bureaucracies or loose associations. Third, showing how the expanded actorhood of the contemporary organization, and the associated interpenetration with the environment, dialectically generate structures far removed from instrumental rationality.
This book provides a general introduction to the R Commander graphical user interface (GUI) to R for readers who are unfamiliar with R. It is suitable for use as a supplementary text in a basic or intermediate-level statistics course. It is not intended to replace a basic or other statistics text but rather to complement it, although it does promote sound statistical practice in the examples. The book should also be useful to individual casual or occasional users of R for whom the standard command-line interface is an obstacle.
Up-to-Date Guidance from One of the Foremost Members of the R Core Team Written by John M. Chambers, the leading developer of the original S software, Extending R covers key concepts and techniques in R to support analysis and research projects. It presents the core ideas of R, provides programming guidance for projects of all scales, and introduces new, valuable techniques that extend R. The book first describes the fundamental characteristics and background of R, giving readers a foundation for the remainder of the text. It next discusses topics relevant to programming with R, including the apparatus that supports extensions. The book then extends R’s data structures through object-oriented programming, which is the key technique for coping with complexity. The book also incorporates a new structure for interfaces applicable to a variety of languages. A reflection of what R is today, this guide explains how to design and organize extensions to R by correctly using objects, functions, and interfaces. It enables current and future users to add their own contributions and packages to R. A 2017 Choice Outstanding Academic Title
Hairy cell Leukaemia (HCL) has always attracted an interest out of all proportion to its frequency and continues to do so. There are two reasons for this. The first is that the disease is unusually responsive to therapy and second is that it has provided a number of important insights into B-cell biology. This monograph is a comprehensive account of hairy cell leukaemia and aims to provide a more detailed account than is available in the existing literature. The work is timely because a consensus has now emerged concerning accurate differential diagnosis a nd curative treatment. These aspects therefore form the focus of the book and are considered in detail. The basic advances in the laboratory that encourage the belief that elucidation of the underlying oncogenic event in the disease may be within reach. The background to this belief is extensively renewed. As a result the monograph will be of interest and practical value to both clinicians and researchers in this and related fields.
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