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Advanced R Statistical Programming and Data Models

Analysis, Machine Learning, and Visualization

  • Matt Wiley
  • Joshua F. Wiley
Book

Table of contents

  1. Front Matter
    Pages i-xx
  2. Matt Wiley, Joshua F. Wiley
    Pages 1-31
  3. Matt Wiley, Joshua F. Wiley
    Pages 33-59
  4. Matt Wiley, Joshua F. Wiley
    Pages 61-122
  5. Matt Wiley, Joshua F. Wiley
    Pages 123-164
  6. Matt Wiley, Joshua F. Wiley
    Pages 165-224
  7. Matt Wiley, Joshua F. Wiley
    Pages 225-249
  8. Matt Wiley, Joshua F. Wiley
    Pages 251-303
  9. Matt Wiley, Joshua F. Wiley
    Pages 305-382
  10. Matt Wiley, Joshua F. Wiley
    Pages 383-433
  11. Matt Wiley, Joshua F. Wiley
    Pages 435-477
  12. Matt Wiley, Joshua F. Wiley
    Pages 479-552
  13. Matt Wiley, Joshua F. Wiley
    Pages 553-586
  14. Matt Wiley, Joshua F. Wiley
    Pages 587-611
  15. Back Matter
    Pages 613-638

About this book

Introduction

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 study.

Written 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.  

You will:
  • Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing
  • Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis
  • Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification
  • Address missing data using multiple imputation in R
  • Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability 

Keywords

R programming statistics data models big data data science

Authors and affiliations

  • Matt Wiley
    • 1
  • Joshua F. Wiley
    • 2
  1. 1.Columbia CityUSA
  2. 2.Columbia CityUSA

Bibliographic information