000			02681nam a22002537a 4500
005			20220318152209.0
008			220318b ||||| |||| 00| 0 eng d
020			_a9781484275979
040			_cAL
041			_aEnglish
082			_223 _a519.5028 _bWILA
100			_aMatt Wiley and others _924209
245			_aAdvanced R Statistical Programming and Data Models: Analysis Machine Learning and Visualization
260			_aUSA _bAPress _c2022
300			_axx,638 p. _bPB _c25.5x17.5 cm.
365			_a6321 _b₹1999.20 _c₹ _d₹2499.00 _e20% _f08-03-2022
520			_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. What You’ll Learn 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 Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).
650			_aR (Computer program language) _924210
650			_a Mathematical statistics -- Data processing _924211
650			_aElectronic books _924212
700			_aWILEY (Matt) _924213
700			_aWILEY (Joshua F) _924214
942			_2ddc _cBK
999			_c221951 _d221951