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Informationen zum Autor Michael Crawley is Professor at Imperial College at Silwood Park. He is a fellow of the Royal Society and author of the bestselling titles "Statistics: An Introduction using R" and Statistical "Computing: An Introduction to Data Analysis Using S-Plus." Klappentext Hugely successful and popular text presenting an extensive and comprehensive guide for all R usersThe R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research.This edition:* Features full colour text and extensive graphics throughout.* Introduces a clear structure with numbered section headings to help readers locate information more efficiently.* Looks at the evolution of R over the past five years.* Features a new chapter on Bayesian Analysis and Meta-Analysis.* Presents a fully revised and updated bibliography and reference section.* Is supported by an accompanying website allowing examples from the text to be run by the user.Praise for the first edition:'...if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.' (The American Statistician, August 2008) 'The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book...' (Professional Pensions, July 2007) Zusammenfassung As one of the most powerful and flexible statistical software packages, R is fast becoming the universal language in which to obtain and present results. Inhaltsverzeichnis Preface vii1 Getting Started 12 Essentials of the R Language 93 Data Input 974 Dataframes 1075 Graphics 1356 Tables 1837 Mathematics 1958 Classical Tests 2799 Statistical Modelling 32310 Regression 38711 Analysis of Variance 44912 Analysis of Covariance 48913 Generalized Linear Models 51114 Count Data 52715 Count Data in Tables 54916 Proportion Data 56917 Binary Response Variables 59318 Generalized Additive Models 61119 Mixed-Effects Models 62720 Non-linear Regression 66121 Meta-analysis xxx22 Bayesian statistics xxx23 Tree Models 68524 Time Series Analysis 70125 Multivariate Statistics 73126 Spatial Statistics 74927 Survival Analysis 78728 Simulation Models 81129 Changing the Look of Graphics 827References and Further Reading 873Index 877s...
