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

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 us...

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Bibliographische Detailangaben
Hauptverfasser: Wiley, Matt, Wiley, Joshua F.
Format: E-Book Buch
Sprache:Englisch
Veröffentlicht: Berkeley, CA Apress, an imprint of Springer Nature 2019
Apress
Apress L. P
Ausgabe:1
Schlagworte:
ISBN:9781484228715, 1484228715, 9781484228722, 1484228723
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:Includes bibliographical references and index
ISBN:9781484228715
1484228715
9781484228722
1484228723
DOI:10.1007/978-1-4842-2872-2