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

• Main Authors: Wiley, Matt, Wiley, Joshua F.
• Format: eBook Book
• Language: English
• Published: Berkeley, CA Apress, an imprint of Springer Nature 2019; Apress; Apress L. P
• Edition: 1
• Subjects: Computer programs; Computer Science; Decision making; General Engineering & Project Administration; General References; Industrial management; Mathematical models; Mathematical statistics > Data processing; Probability and Statistics in Computer Science; Professional and Applied Computing; Programming Languages, Compilers, Interpreters; Programming Techniques; R (Computer program language); Statistical methods
• ISBN: 9781484228715; 1484228715; 9781484228722; 1484228723
• DOI: 10.1007/978-1-4842-2872-2

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 this book 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.