Mastering Scientific Computing with R
Paul Gerrard Radia M. Johnson更新时间：2021-08-06 19:05:19
最新章节：Indexcoverpage
Mastering Scientific Computing with R
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Support files eBooks discount offers and more
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. Programming with R
Data structures in R
Loading data into R
Basic plots and the ggplot2 package
Flow control
Functions
General programming and debugging tools
Summary
Chapter 2. Statistical Methods with R
Descriptive statistics
Probability distributions
Fitting distributions
Hypothesis testing
Summary
Chapter 3. Linear Models
An overview of statistical modeling
Linear regression
Analysis of variance
Generalized linear models
Generalized additive models
Linear discriminant analysis
Principal component analysis
Clustering
Summary
Chapter 4. Nonlinear Methods
Nonparametric and parametric models
The adsorption and body measures datasets
Theory-driven nonlinear regression
Visually exploring nonlinear relationships
Extending the linear framework
Nonparametric nonlinear methods
Nonparametric methods with the np package
Summary
Chapter 5. Linear Algebra
Matrices and linear algebra
The physical functioning dataset
Basic matrix operations
Triangular matrices
Matrix decomposition
Applications
Summary
Chapter 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
Summary
Chapter 7. Structural Equation Modeling and Confirmatory Factor Analysis
Datasets
The basic ideas of SEM
Matrix representation of SEM
SEM model fitting and estimation methods
Comparing OpenMx to lavaan
Summary
Chapter 8. Simulations
Basic sample simulations in R
Pseudorandom numbers
Monte Carlo simulations
Monte Carlo integration
Rejection sampling
Importance sampling
Simulating physical systems
Summary
Chapter 9. Optimization
One-dimensional optimization
Linear programming
Quadratic programming
General non-linear optimization
Other optimization packages
Summary
Chapter 10. Advanced Data Management
Cleaning datasets in R
String processing and pattern matching
Floating point operations and numerical data types
Memory management in R
Missing data
The Amelia package
The mice package
Summary
Index
更新时间：2021-08-06 19:05:19