File Name: an r and s plus companion to multivariate analysis .zip
NOTE: modifications to this page have been suspended while the R webmasters consider how, or whether, to maintain the page in the future. Flexible Imputation of Missing Data. ISBN
NOTE: modifications to this page have been suspended while the R webmasters consider how, or whether, to maintain the page in the future. Flexible Imputation of Missing Data. ISBN Oceanographic Analysis with R. Springer-Verlag, New York, October This package greatly simplifies oceanographic analysis by handling the details of discipline-specific file formats, calculations, and plots.
Designed for real-world application and developed with open-source protocols, oce supports a broad range of practical work. Generic functions take care of general operations such as subsetting and plotting data, while specialized functions address more specific tasks such as tidal decomposition, hydrographic analysis, and ADCP coordinate transformation. In addition, the package makes it easy to document work, because its functions automatically update processing logs stored within its data objects.
Kelley teaches key R functions using classic examples from the history of oceanography, specifically the work of Alfred Redfield, Gordon Riley, J. Tuzo Wilson, and Walter Munk. Including a suite of real-life applications and over exercises and solutions, the treatment is ideal for oceanographers, technicians, and students who want to add R to their list of tools for oceanographic analysis.
European Scientific Institute, Data Visualisation with R. Springer International Publishing, New York, Every example uses real data and includes step-by-step explanations of the figures and their programming. Apprendre R en un Jour. SJ Murray, Learning Base R. Lightning Source, It introduces the key topics to begin analyzing data and programming in R. The focus is on the R language rather than a particular application. The book can be used for self-study or an introductory class on R.
Nearly exercises make this book appropriate for a classroom setting. Springer, The great advantage of R is that it is free, extremely flexible and extensible. The book addresses the specific needs of economists, and helps them move up the R learning curve. It covers some mathematical topics such as, graphing the Cobb-Douglas function, using R to study the Solow growth model, in addition to statistical topics, from drawing statistical graphs to doing linear and logistic regression.
It uses data that can be downloaded from the internet, and which is also available in different R packages.
With some treatment of basic econometrics, the book discusses quantitative economics broadly and simply, looking at models in the light of data. Students of economics or economists keen to learn how to use R would find this book very useful. Empirical Research in Economics: Growing up with R. At present, there is a lack of integration among course work, research methodology, and software usage in statistical analysis of economic data.
The objective of this book is to help young professionals conduct an empirical study in economics over a reasonable period, with the expectation of four months in general. In German. Introduction to statistical data analysis with R. The analyses are performed and discussed using real data.
After a brief description of the statistical software R, important parameters and diagrams of descriptive statistics are introduced. Subsequently, recommendations for generating diagrams are provided, where special attention is given to the selection of appropriate colors.
The second half of the book addresses the basics of inferential statistics. First, a number of probability distributions are introduced and their applicability is illustrated by examples.
Next, the book describes how the parameters of these distributions, which are unknown in practice, may be estimated from given data. The final chapter introduces statistical tests and reviews the most important tests for practical applications. Mastering Data Analysis with R. Packt Publishing, 9 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.
Datendesign mit R. Using R for Statistics. Apress, If you need to use a variety of statistics, then Using R for Statistics will get you the answers to most of the problems you are likely to encounter. Using R for Statistics is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests.
The book walks you through R basics and how to use R to accomplish a wide variety statistical operations. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background.
After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests.
No prior knowledge of R or of programming is assumed, though you should have some experience with statistics. You should have some prior experience with statistics, however. John Wiley, New Jersey, It summarizes the basic concepts and ideas of analyzing multivariate dependent data, provides econometric and statistical models useful for describing the dynamic dependence between variables, discusses the identifiability problem when the models become too flexible, introduces ways to search for simplifying structure hidden in high-dimensional time series, addresses the applicabilities and limitations of multivariate time series methods, and, equally important, develops the R MTS package for readers to apply the methods and models discussed in the book.
The vector autoregressive models and multivariate volatility models are discussed and demonstrated. Wiley, Optimization is an important tool for decision science and for the analysis of physical systems used in engineering. Nonlinear Parameter Optimization with R explores the principal tools available in R for function minimization, optimization, and nonlinear parameter determination and features numerous examples throughout.
Statistics: An Introduction using R. Wiley, 2nd edition, Ellipses, 1st edition, Psychologie statistique avec R. Pratique R. Springer, Paris, Although many authors have recently advocated for the use of bayesian statistics in psychology Wagenmaker et al. This manual provides a full bayesian toolbox for commonly encountered problems in psychology and social sciences, for comparing proportions, variances and means, and discusses the advantages.
But all foundations of the frequentist approach are also provided, from data description to probability and density, through combinatorics and set algebra. A special emphasis has been put on the analysis of categorical data and contingency tables.
Binomial and multinomial models with beta and Dirichlet priors are presented, and their use for making between rows or between cells contrasts in contingency tables is detailed on real data.
In addition to classical and Bayesian inference on means, direct and Bayesian inference on effect size and standardized effects are presented, in agreement with recent APA recommendations.
Dynamic Documents with R and knitr. The reports range from homework, projects, exams, books, blogs, and web pages to any documents related to statistical graphics, computing, and data analysis.
For beginners, the text provides enough features to get started on basic applications. For power users, the last several chapters enable an understanding of the extensibility of the knitr package. Learn R in a Day. The book assumes no prior knowledge of computer programming and progressively covers all the essential steps needed to become confident and proficient in using R within a day. Topics include how to input, manipulate, format, iterate loop , query, perform basic statistics on, and plot data, via a step-by-step technique and demonstrations using in-built datasets which the reader is encouraged to replicate on their computer.
Each chapter also includes exercises with solutions to practice key skills and empower the reader to build on the essentials gained during this introductory course.
It focuses on scalar financial time series with applications. High-frequency data and volatility models are discussed. The book also uses case studies to illustrate the application of modeling financial data. Analyse von Genexpressionsdaten mit R und Bioconductor.
Ventus Publishing ApS, London, See web site , March R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating.
Introductory R is written for the novice user who knows a bit about statistics but who hasn't yet got to grips with the ways of R. This book: walks you through the basics of R's command line interface; gives a set of simple rules to follow to make sure you import your data properly; introduces the script editor and gives advice on workflow; contains a detailed introduction to drawing graphs in R and gives advice on how to deal with some of the most common errors that you might encounter.
The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results. Methods of Statistical Model Estimation. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting.
The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method.
Applied Multivariate Statistical Analysis , 5th ed. An R and S-plus companion to multivariate analysis. In other words, you should know linear algebra and statistical principles at the level of Casella and. The examples and assignments will involve some computing. It will useful to learn one of the. There will be biweekly homework assignments. The assignments are due in class.
It seems that you're in Germany. We have a dedicated site for Germany. Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to be examined simultaneously to get the most informative results. This requires the use of one or other of the many methods of multivariate analysis, and the use of a suitable software package such as S-PLUS or R.
Many of the books have web pages associated with them that have the data files for the book and web pages showing how to perform the analyses from the book using packages like SAS, Stata, SPSS, etc. Please see our Textbook Examples page. Box, William G.
Finally, data from were used to validate the predictive models. These considerations, joined with a reduction in the cost of computer time, the increased availability of multivariate statistical packages, and an increased willingness on the part of ecologists to use mathematics, Books about Multivariate Statistical Methods in Behavioral Research, The book presents multivariate statistical methods useful in geological analysis. Yet, the efficiency of morphological decomposition seems to vary for different languages and morphological types, as well as for first and second language speakers. Reaction times RTs are an important source of information in experimental psychology.
Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to be examined simultaneously to get the most informative results. This requires the use of one or other of the many methods of multivariate analysis, and the use of a suitable software package such as S-PLUS or R. In this book the core multivariate methodology is covered along with some basic theory for each method described.
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Everitt. p. cm.—(Springer texts in statistics). Includes bibliographical references and index. ISBN (alk. paper). 1. Multivariate analysis. 2. S-Plus. 3. R.Jeanette V. 22.05.2021 at 09:15
Ideal for courses on multivariate statistics found in psychology, education, sociology, and business departments, the book also appeals to practicing researchers with little or no training in multivariate methods.Publia F. 27.05.2021 at 05:13
Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to be examined simultaneously to get the most informative results.Fulvio P. 28.05.2021 at 01:19
Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to An R and S-PLUS® Companion to Multivariate Analysis Brian Sidney Everitt. Pages PDF · Principal Components Analysis.