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010   $a9781484228722
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024 7 $a10.1007/978-1-4842-2872-2
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100   $a20190220d2019      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAdvanced R statistical programming and data models $eanalysis, machine learning, and visualization /$fby Matt Wiley, Joshua F. Wiley
205   $a1st ed. 2019.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2019.
215   $a1 online resource (XX, 638 p. 207 illus., 127 illus. in color.)
311 1 $a9781484228715 
311 1 $a1-4842-2871-5 
327   $a1 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.
330   $aCarry 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 .
606   $aR (Llenguatge de programaciķ)$2lemac
606   $aEstadística matemātica$2lemac
606   $aProgramming languages (Electronic computers)
606   $aComputer programming
606   $aMathematical statistics
606   $aR (Computer program language)
606   $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037
606   $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010
606   $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036
615  7$aR (Llenguatge de programaciķ)
615  7$aEstadística matemātica
615  0$aProgramming languages (Electronic computers).
615  0$aComputer programming.
615  0$aMathematical statistics.
615  0$aR (Computer program language).
615 14$aProgramming Languages, Compilers, Interpreters.
615 24$aProgramming Techniques.
615 24$aProbability and Statistics in Computer Science.
676   $a005.13
700   $aWiley$b Matt$4aut$4http://id.loc.gov/vocabulary/relators/aut$0897297
702   $aWiley$b Joshua F$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910338002703321
996   $aAdvanced R Statistical Programming and Data Models$92534295
997   $aUNINA