Office of Academic Resources
Chulalongkorn University
Chulalongkorn University

Home / Help

TitleAdvanced R Statistical Programming and Data Models [electronic resource] : Analysis, Machine Learning, and Visualization / by Matt Wiley, Joshua F. Wiley
ImprintBerkeley, CA : Apress : Imprint: Apress, 2019
Edition 1st ed. 2019
Connect tohttps://doi.org/10.1007/978-1-4842-2872-2
Descript XX, 638 p. 207 illus., 127 illus. in color. online resource

SUMMARY

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


CONTENT

1 Univariate Data Visualization -- 2 Multivariate Data Visualization -- 3 Generalized Linear Models 1 -- 4 Generalized Linear Models 2 -- 5 Generalized Additive Models -- 6 Machine Learning: Introduction -- 7 Machine Learning: Unsupervised -- 8 Machine Learning: Supervised -- 9 Missing Data -- 10 Generalized Linear Mixed Models: Introduction -- 11 Generalized Linear Mixed Models: Linear -- 12 Generalized Linear Mixed Models: Advanced -- 13 Modeling IIV -- Bibliography


Computer science Programming Languages Compilers Interpreters. http://scigraph.springernature.com/things/product-market-codes/I14037 Programming Techniques. http://scigraph.springernature.com/things/product-market-codes/I14010 Probability and Statistics in Computer Science. http://scigraph.springernature.com/things/product-market-codes/I17036



Location



Office of Academic Resources, Chulalongkorn University, Phayathai Rd. Pathumwan Bangkok 10330 Thailand

Contact Us

Tel. 0-2218-2929,
0-2218-2927 (Library Service)
0-2218-2903 (Administrative Division)
Fax. 0-2215-3617, 0-2218-2907

Social Network

  line

facebook   instragram