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  • Book Overview & Buying Mastering Scientific Computing with R
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Mastering Scientific Computing with R

Mastering Scientific Computing with R

By : Paul Gerrard
3.6 (7)
Buy this Book
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Mastering Scientific Computing with R

Mastering Scientific Computing with R

3.6 (7)
By: Paul Gerrard
Buy this Book

Overview of this book

If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Programming with R
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1. Programming with R
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Data structures in R
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Loading data into R
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Basic plots and the ggplot2 package
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Flow control
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Functions
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General programming and debugging tools
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Summary
2
2. Statistical Methods with R
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2. Statistical Methods with R
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Descriptive statistics
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Probability distributions
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Fitting distributions
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Hypothesis testing
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Summary
3
3. Linear Models
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3. Linear Models
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An overview of statistical modeling
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Linear regression
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Analysis of variance
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Generalized linear models
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Generalized additive models
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Linear discriminant analysis
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Principal component analysis
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Clustering
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Summary
4
4. Nonlinear Methods
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4. Nonlinear Methods
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Nonparametric and parametric models
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The adsorption and body measures datasets
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Theory-driven nonlinear regression
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Visually exploring nonlinear relationships
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Extending the linear framework
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Nonparametric nonlinear methods
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Nonparametric methods with the np package
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Summary
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5. Linear Algebra
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5. Linear Algebra
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Matrices and linear algebra
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The physical functioning dataset
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Basic matrix operations
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Triangular matrices
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Matrix decomposition
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Applications
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Summary
6
6. Principal Component Analysis and the Common Factor Model
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6. Principal Component Analysis and the Common Factor Model
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A primer on correlation and covariance structures
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Datasets used in this chapter
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Principal component analysis and total variance
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Formative constructs using PCA
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Exploratory factor analysis and reflective constructs
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Summary
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7. Structural Equation Modeling and Confirmatory Factor Analysis
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7. Structural Equation Modeling and Confirmatory Factor Analysis
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Datasets
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The basic ideas of SEM
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Matrix representation of SEM
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SEM model fitting and estimation methods
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Comparing OpenMx to lavaan
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Summary
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8. Simulations
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8. Simulations
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Basic sample simulations in R
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Pseudorandom numbers
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Monte Carlo simulations
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Monte Carlo integration
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Rejection sampling
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Importance sampling
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Simulating physical systems
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Summary
9
9. Optimization
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9. Optimization
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One-dimensional optimization
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Linear programming
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Quadratic programming
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General non-linear optimization
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Other optimization packages
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Summary
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10. Advanced Data Management
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10. Advanced Data Management
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Cleaning datasets in R
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String processing and pattern matching
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Floating point operations and numerical data types
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Memory management in R
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Missing data
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The Amelia package
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The mice package
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Summary
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Index
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Index

Summary

Hopefully, throughout this chapter, you learned a variety of methods and techniques to optimize a variety of functions. We covered how to use a wide range of R packages and functions to set up, solve, and visualize different optimization problems, so you should be ready to give your own minimization and maximization problems a try. Now that you are much more familiar with R and its packages for scientific computing, we are ready to move on to our last chapter, which will teach you to clean up and manage your data for efficient programming and analysis in R.

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Tech Concepts
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Programming languages
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Mastering Scientific Computing with R
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