Data Analysis and Graphics Using R R语言数据分析与制图

图书标准信息

• 作者 John（约翰·梅因唐纳德）、John Braun（约翰·布劳恩） 著
• 出版社 Cambridge University Press
• 出版时间 2006-09
• 版次 1
• ISBN 9780521861168
• 定价 653.70元
• 装帧 精装
• 开本 16开
• 纸张 胶版纸
• 页数 528页
• 正文语种 英语
• 丛书 Cambridge Series in Statistical and Probabilistic Mathematics； 10

【内容简介】

Jointherevolutionignitedbytheground-breakingRsystem!StartingwithanintroductiontoR,coveringstandardregressionmethods,thenpresentingmoreadvancedtopics,thisbookguidesusersthroughthepracticalandpowerfultoolsthattheRsystemprovides.Theemphasisisonhands-onanalysis,graphicaldisplayandinterpretationofdata.Themanyworkedexamples,takenfromreal-worldresearch,areaccompaniedbycommentaryonwhatisdoneandwhy.Awebsiteprovidescomputercodeanddatasets,allowingreaderstoreproduceallanalyses.Updatesandsolutionstoselectedexercisesarealsoavailable.Assumingonlybasicstatisticalknowledge,thebookisidealforresearchscientists,final-yearundergraduateorgraduatelevelstudentsofappliedstatistics,andpractisingstatisticians.Itisbothforlearningandforreference.ThisrevisededitionreflectschangesinRsince2003andhasnewmaterialonsurvivalanalysis,randomcoefficientmodels,andthehandlingofhigh-dimensionaldata.

【作者简介】

JohnMaindonaldisVisitingFellowattheMathematicalSciencesInstitute,AustralianNationalUniversity.Hehascollaboratedextensivelywithscientistsinawiderangeofapplicationareas,frommedicineandpublichealth,topopulationgenetics,machinelearning,economichistory,andforensiclinguistics.JohnBraunisAssociateProfessorofStatisticalandActuarialSciences,UniversityofWesternOntario.Hehascollaboratedwithbiostatisticians,biologists,psychologistsandmostrecentlyhasbecomeinvolvedwithanetworkofforestryresearchers.

【目录】

Preface

1. A brief introduction to R

2. Styles of data analysis

3. Statistical models

4. An introduction to formal inference

5. Regression with a single predictor

6. Multiple linear regression

7. Exploiting the linear model framework

8. Generalized linear models and survival analysis

9. Time series models

10. Multi-level models and repeated measures

11. Tree-based classification and regression

12. Multivariate data exploration and discrimination

13. Regression on principal component or discriminant scores

14. The R system - additional topics

Epilogue - models

References

Index of R symbols and functions

Index of terms

Index of names