Mastering Scientific Computing with R

Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment

Mastering Scientific Computing with R

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Paul Gerrard, Radia M. Johnson

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Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment
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Book Details

ISBN 139781783555253
Paperback432 pages

Book Description

With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions.

Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method.

Table of Contents

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

What You Will Learn

  • Master data management in R
  • Perform hypothesis tests using both parametric and nonparametric methods
  • Understand how to perform statistical modeling using linear methods
  • Model nonlinear relationships in data with kernel density methods
  • Use matrix operations to improve coding productivity
  • Utilize the observed data to model unobserved variables
  • Deal with missing data using multiple imputations
  • Simplify high-dimensional data using principal components, singular value decomposition, and factor analysis

Authors

Table of Contents

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

Book Details

ISBN 139781783555253
Paperback432 pages
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