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Mastering Scientific Computing with R

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Mastering Scientific Computing with R

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Mastering Scientific Computing with R
Jan, 2015 | 432 pages
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Table of Contents (17 chapters)
Mastering Scientific Computing with R
Mastering Scientific Computing with R
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Programming with R
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
2
Statistical Methods with R
Statistical Methods with R
Descriptive statistics
Probability distributions
Fitting distributions
Hypothesis testing
Summary
3
Linear Models
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
4
Nonlinear Methods
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
5
Linear Algebra
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
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
7
Structural Equation Modeling and Confirmatory Factor Analysis
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
8
Simulations
Simulations
Basic sample simulations in R
Pseudorandom numbers
Monte Carlo simulations
Monte Carlo integration
Rejection sampling
Importance sampling
Simulating physical systems
Summary
9
Optimization
Optimization
One-dimensional optimization
Linear programming
Quadratic programming
General non-linear optimization
Other optimization packages
Summary
10
Advanced Data Management
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
Index

Credits

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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