- Mastering Scientific Computing with R
- Table of Contents
- Mastering Scientific Computing with R
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Programming with R
- 2. Statistical Methods with R
- 3. Linear Models
- 4. Nonlinear Methods
- 5. Linear Algebra
- Matrices and linear algebra
- The physical functioning dataset
- Basic matrix operations
- Triangular matrices
- Matrix decomposition
- Applications
- Summary
- 6. Principal Component Analysis and the Common Factor Model
- A primer on correlation and covariance structures
- Datasets used in this chapter
- Principal component analysis and total variance
- Formative constructs using PCA
- Exploratory factor analysis and reflective constructs
- Familiarizing yourself with the basic terms
- Matrices of interest
- Basic EFA and concepts of covariance algebra
- Advanced EFA with the psych package
- Summary
- 7. Structural Equation Modeling and Confirmatory Factor Analysis
- 8. Simulations
- 9. Optimization
- 10. Advanced Data Management
- Index
Authors
Paul Gerrard
Radia M. Johnson
Reviewers
Laurent Drouet
Ratanlal Mahanta
Mzabalazo Z. Ngwenya
Donato Teutonico
Commissioning Editor
Kartikey Pandey
Acquisition Editor
Greg Wild
Content Development Editor
Akshay Nair
Technical Editors
Rosmy George
Ankita Thakur
Copy Editors
Shivangi Chaturvedi
Pranjali Chury
Puja Lalwani
Adithi Shetty
Project Coordinator
Mary Alex
Proofreaders
Simran Bhogal
Martin Diver
Ameesha Green
Paul Hindle
Bernadette Watkins
Indexer
Priya Subramani
Graphics
Sheetal Aute
Disha Haria
Abhinash Sahu
Production Coordinator
Conidon Miranda
Cover Work
Conidon Miranda
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