9781484228715-1484228715-Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization

Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization

ISBN-13: 9781484228715
ISBN-10: 1484228715
Edition: 1st ed.
Author: Wiley, Matt, Wiley, Joshua F.
Publication date: 2019
Publisher: Apress
Format: Paperback 658 pages
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Book details

ISBN-13: 9781484228715
ISBN-10: 1484228715
Edition: 1st ed.
Author: Wiley, Matt, Wiley, Joshua F.
Publication date: 2019
Publisher: Apress
Format: Paperback 658 pages

Summary

Acknowledged authors Wiley, Matt, Wiley, Joshua F. wrote Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization comprising 658 pages back in 2019. Textbook and eTextbook are published under ISBN 1484228715 and 9781484228715. Since then Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

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).
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