Carry Out A Variety Of Advanced Statistical Analyses Including Generalized Additive Models, Mixed Effects Models, Multiple Imputation, Machine Learning, And Missing Data Techniques Using R Each Chapter Starts With Conceptual Background Information About The Techniques, Includes Multiple Examples Using R To Achieve Results, And Concludes With A Case StudyWritten By Matt And Joshua F Wiley, Advanced R Statistical Programming And Data Models Shows You How To Conduct Data Analysis Using The Popular R Language You Ll Delve Into The Preconditions Or Hypothesis For Various Statistical Tests And Techniques And Work Through Concrete Examples Using R For A Variety Of These Next Level Analytics This Is A Must Have Guide And Reference On Using And Programming With The R Language What You Ll LearnConduct Advanced Analyses In R Including Generalized Linear Models, Generalized Additive Models, Mixed Effects Models, Machine Learning, And Parallel ProcessingCarry Out Regression Modeling Using R Data Visualization, Linear And Advanced Regression, Additive Models, Survival Time To Event AnalysisHandle Machine Learning Using R Including Parallel Processing, Dimension Reduction, And Feature Selection And ClassificationAddress Missing Data Using Multiple Imputation In RWork On Factor Analysis, Generalized Linear Mixed Models, And Modeling Intraindividual Variability Who This Book Is For Working Professionals, Researchers, Or Students Who Are Familiar With R And Basic Statistical Techniques Such As Linear Regression And Who Want To Learn How To Use R To Perform Advanced Analytics Particularly, Researchers And Data Analysts In The Social Sciences May Benefit From These Techniques Additionally, Analysts Who Need Parallel Processing To Speed Up Analytics Are Given Proven Code To Reduce Time To Result S
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- 638 pages
- Advanced R Statistical Programming and Data Models
- Matt Wiley
- 12 July 2017 Matt Wiley