Programming with R Part-3

Software for Data Analysis Programming with R
by John M. Chambers

The R version of S4 and other R techniques. This book guides the reader in programming with R, from interactive use and writing simple functions to the design of R packages and intersystem interfaces.
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Wavelet Methods in Statistics with R (Use R)
by G.P. Nason

Wavelet methods have recently undergone a rapid period of development with important implications for a number of disciplines including statistics. This book fulfils three purposes. First, it is a gentle introduction to wavelets and their uses in statistics. Second, it acts as a quick and broad reference to many recent developments in the area. The book concentrates on describing the essential elements and provides comprehensive source material references. Third, the book intersperses R code that explains and demonstrates both wavelet and statistical methods. The code permits the user to learn the methods, to carry out their own analyses and further develop their own methods. The book is designed to be read in conjunction with WaveThresh4, the freeware R package for wavelets. The book introduces the wavelet transform by starting with the simple Haar wavelet transform and then builds to consider more general wavelets such as the Daubechies compactly supported series. The book then describes the evolution of wavelets in the directions of complex-valued wavelets, non-decimated transforms, multiple wavelets and wavelet packets as well as giving consideration to boundary conditions initialization. Later chapters explain the role of wavelets in nonparametric regression problems via a variety of techniques including thresholding, cross-validation, SURE, false-discovery rate and recent Bayesian methods, and also consider how to deal with correlated and non-Gaussian noise structures. The book also looks at how nondecimated and packet transforms can improve performance. The penultimate chapter considers the role of wavelets in both stationary and non-stationary time series analysis. The final chapter describes recent work concerning the role of wavelets for variance stabilization for non-Gaussian intensity estimation. The book is aimed at final year undergraduate and Masters students in a numerate discipline (such as mathematics, statistics, physics, economics and engineering) and would also suit as a quick reference for postgraduate or research level activity. The book would be ideal for a researcher to learn about wavelets, to learn how to use wavelet software and then to adapt the ideas for their own purposes.
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Statistical Data Analysis Explained Applied Environmental Statistics with R
by Clemens Reimann

Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology. The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.
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Morphometrics with R (Use R)
by Julien Claude

Quantifying shape and size variation is essential in evolutionary biology and in many other disciplines. Since the “morphometric revolution of the 90s,” an increasing number of publications in applied and theoretical morphometrics emerged in the new discipline of statistical shape analysis. The R language and environment offers a single platform to perform a multitude of analyses from the acquisition of data to the production of static and interactive graphs. This offers an ideal environment to analyze shape variation and shape change. This open-source language is accessible for novices and for experienced users. Adopting R gives the user and developer several advantages for performing morphometrics: evolvability, adaptability, interactivity, a single and comprehensive platform, possibility of interfacing with other languages and software, custom analyses, and graphs. The book explains how to use R for morphometrics and provides a series of examples of codes and displays covering approaches ranging from traditional morphometrics to modern statistical shape analysis such as the analysis of landmark data, Thin Plate Splines, and Fourier analysis of outlines. The book fills two gaps: the gap between theoreticians and students by providing worked examples from the acquisition of data to analyses and hypothesis testing, and the gap between user and developers by providing and explaining codes for performing all the steps necessary for morphometrics rather than providing a manual for a given software or package. Students and scientists interested in shape analysis can use the book as a reference for performing applied morphometrics, while prospective researchers will learn how to implement algorithms or interfacing R for new methods. In addition, adopting the R philosophy will enhance exchanges within and outside the morphometrics community. Julien Claude is evolutionary biologist and palaeontologist at the University of Montpellier 2 where he got his Ph.D. in 2003. He works on biodiversity and phenotypic evolution of a variety of organisms, especially vertebrates. He teaches evolutionary biology and biostatistics to undergraduate and graduate students and has developed several functions in R for the package APE.
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Applied Econometrics with R (Use R)
by Christian Kleiber

This is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research. An R package accompanying this book, AER, is available from the Comprehensive R Archive Network (CRAN) at http://CRAN.R-project.org/package=AER. It contains some 100 data sets taken from a wide variety of sources, the full source code for all examples used in the text plus further worked examples, e.g., from popular textbooks. The data sets are suitable for illustrating, among other things, the fitting of wage equations, growth regressions, hedonic regressions, dynamic regressions and time series models as well as models of labor force participation or the demand for health care. The goal of this book is to provide a guide to R for users with a background in economics or the social sciences. Readers are assumed to have a background in basic statistics and econometrics at the undergraduate level. A large number of examples should make the book of interest to graduate students, researchers and practitioners alike.
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A First Course in Statistical Programming with R
by W. John Braun

This book introduces students to statistical programming, using R as a basis. Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects.
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Multiple Testing Procedures with Applications to Genomics
by Sandrine Dudoit

This book provides a detailed account of the theoretical foundations of proposed multiple testing methods and illustrates their application to a range of testing problems in genomics.
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Correspondence Analysis in Practice, Second Edition
by Michael Greenacre

This book shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. T his completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries. It includes a computational appendix that provides the R commands that correspond to most of the analyses featured in the book.
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Data Analysis and Graphics Using R An Example-based Approach
by John Maindonald

Following a brief introduction to R, this has extensive examples that illustrate practical data analysis using R. There is extensive advice on practical data analysis. Topics covered include exploratory data analysis, tests and confidence intervals, regression, genralized linear models, survival analysis, time series, multi-level models, trees and random forests, classification, and ordination.
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Bayesian Core A Practical Approach to Computational Bayesian Statistics
by Jean-Michel Marin

This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. While R programs are provided on the book website and R hints are given in the computational sections of the book, The Bayesian Core requires no knowledge of the R language and it can be read and used with any other programming language.
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Interactive and Dynamic Graphics for Data Analysis with R and GGobi

This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models. All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples
by Dianne Cook
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The Statistics of Gene Mapping
by David Siegmund

This book details the statistical concepts used in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The R programming language is developed in the text.
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