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. 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).
