Authors:
- Demonstrates applied R programming to make analyses more efficient and effective
- Shows how to handle machine learning using R
- Includes case studies throughout book
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Table of contents (13 chapters)
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Front Matter
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Back Matter
About this book
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, mixedeffects 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 givenproven code to reduce time to result(s).
Authors and Affiliations
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Columbia City, USA
Matt Wiley, Joshua F. Wiley
About the authors
Joshua F. Wiley is a lecturer in the Monash Institute for Cognitive and Clinical Neurosciences and School of Psychological Sciences at Monash University and a senior partner at Elkhart Group Limited, a statistical consultancy. He earned his PhD from the University of California, Los Angeles, and his research focuses on using advanced quantitative methods to understand the complex interplays of psychological, social, and physiological processes in relation to psychological and physical health. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. Through consulting at Elkhart Group Limited and former work at the UCLA Statistical Consulting Group, he has supported a wide array of clients ranging from graduate students, to experienced researchers, and biotechnology companies. He also develops or co-develops a number of R packages including varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.
Bibliographic Information
Book Title: Advanced R Statistical Programming and Data Models
Book Subtitle: Analysis, Machine Learning, and Visualization
Authors: Matt Wiley, Joshua F. Wiley
DOI: https://doi.org/10.1007/978-1-4842-2872-2
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Matt Wiley and Joshua F. Wiley 2019
Softcover ISBN: 978-1-4842-2871-5Published: 21 February 2019
eBook ISBN: 978-1-4842-2872-2Published: 20 February 2019
Edition Number: 1
Number of Pages: XX, 638
Number of Illustrations: 80 b/w illustrations, 127 illustrations in colour
Topics: Programming Languages, Compilers, Interpreters, Programming Techniques, Probability and Statistics in Computer Science