- 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
-
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