The R book

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The R book

Michael J. Crawley

John Wiley, c2007

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Includes bibliographical references and index

Description and Table of Contents

Description

The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis. Building on the success of the author's bestselling Statistics: An Introduction using R, The R Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines.* Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities.* Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test.* Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences.

Table of Contents

Preface. 1 Getting Started. 2 Essentials of the R Language. 3 Data Input. 4 Dataframes. 5 Graphics. 6 Tables. 7 Mathematics. 8 Classical Tests. 9 Statistical Modelling. 10 Regression. 11 Analysis of Variance. 12 Analysis of Covariance. 13 Generalized Linear Models. 14 Count Data. 15 Count Data in Tables. 16 Proportion Data. 17 Binary Response Variables. 18 Generalized Additive Models. 19 Mixed-Effects Models. 20 Non-linear Regression. 21 Tree Models. 22 Time Series Analysis. 23 Multivariate Statistics. 24 Spatial Statistics. 25 Survival Analysis. 26 Simulation Models. 27 Changing the look of graphics. References and Further Reading. Index.

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