Advanced R statistical programming and data models : analysis, machine learning, and visualization

• Main Authors: Wiley, Matt, (Author), Wiley, Joshua F., (Author)
• Format: eBook
• Language: English
• Published: [Berkeley, CA] : Apress, 2019.
• Subjects: R (Computer program language); Statistics > Data processing.; Mathematical statistics > Data processing.; elektronické knihy; electronic books
• ISBN: 9781484228722; 1484228723; 9781484228715; 1484228715; 9781484228739; 1484228731
• Physical Description: 1 online resource (xx, 638 pages) : illustrations (some color)
• Access: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty

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. You will: 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.