Advanced R Statistical Programming and Data Models

Analysis, Machine Learning, and Visualization

Matt Wiley, Joshua F. Wiley

Research output: Book/ReportBookOtherpeer-review

Abstract

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.
What You’ll Learn

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
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 more 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).
Original languageEnglish
Place of PublicationBerkeley, CA
PublisherApress
Number of pages638
Edition1st
ISBN (Electronic)9781484228722
ISBN (Print)9781484228715
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • R
  • programming
  • statistics
  • data
  • models
  • big data
  • data science

Cite this

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title = "Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization",
abstract = "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. 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 more 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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Advanced R Statistical Programming and Data Models : Analysis, Machine Learning, and Visualization. / Wiley, Matt; Wiley, Joshua F.

1st ed. Berkeley, CA : Apress, 2019. 638 p.

Research output: Book/ReportBookOtherpeer-review

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