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ADVANCED R STATISTICAL PROGRAMMING AND DATA MODELS IBD

JOSHUA F WILEY / MATT WILEY
APRESS
02 / 2019
9781484228715
Inglés

Sinopsis

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. YouâÇÖll 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. áThis is a must-have guide and reference on using and programming with the R language. áWhat YouâÇÖll LearnConduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processingCarry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysisHandle machine learning using R including parallel processing, dimension reduction, and feature selection and classificationAddress missing data using multiple imputation in RWork on factor analysis, generalized linear mixed models, and modeling intraindividual variabilityáWho This Book Is ForáWorking 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).