Analysis, Machine Learning, and Visualization
Buch, Englisch, 638 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 1221 g
ISBN: 978-1-4842-2871-5
Verlag: Apress
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, mixedeffects 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
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 givenproven code to reduce time to result(s).
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
- Mathematik | Informatik Mathematik Stochastik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmierung: Methoden und Allgemeines
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
Weitere Infos & Material
1 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.