04370nam 22006375 450 991033800270332120230907153413.097814842287221-5231-5031-91-4842-2872-310.1007/978-1-4842-2872-2(CKB)4100000007656497(DE-He213)978-1-4842-2872-2(MiAaPQ)EBC5718497(CaSebORM)9781484228722(PPN)23500734X(EXLCZ)99410000000765649720190220d2019 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierAdvanced R statistical programming and data models analysis, machine learning, and visualization /by Matt Wiley, Joshua F. Wiley1st ed. 2019.Berkeley, CA :Apress :Imprint: Apress,2019.1 online resource (XX, 638 p. 207 illus., 127 illus. in color.)9781484228715 1-4842-2871-5 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.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 .R (Llenguatge de programació)lemacEstadística matemàticalemacProgramming languages (Electronic computers)Computer programmingMathematical statisticsR (Computer program language)Programming Languages, Compilers, Interpretershttps://scigraph.springernature.com/ontologies/product-market-codes/I14037Programming Techniqueshttps://scigraph.springernature.com/ontologies/product-market-codes/I14010Probability and Statistics in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17036R (Llenguatge de programació)Estadística matemàticaProgramming languages (Electronic computers).Computer programming.Mathematical statistics.R (Computer program language).Programming Languages, Compilers, Interpreters.Programming Techniques.Probability and Statistics in Computer Science.005.13Wiley Mattauthttp://id.loc.gov/vocabulary/relators/aut897297Wiley Joshua Fauthttp://id.loc.gov/vocabulary/relators/autBOOK9910338002703321Advanced R Statistical Programming and Data Models2534295UNINA