Description 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.
He earned degre.
Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).
About the Author Matt Wiley is a tenured, associate professor of mathematics with awards in both mathematics education and honour student engagement.
Particularly, researchers and Data analysts in the social sciences may benefit from these techniques.
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 RWork 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.
This is a must-have guide and reference on using and Programming with the 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.
Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct Data Analysis using the popular R language.
Written by Matt and Joshua F.
Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study.
Description 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
