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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
5
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
7
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
8
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
10
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
11
Index
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Index

The adsorption and body measures datasets

In this section, we will use two datasets:

  • The adsorption dataset (adsorption.txt) is available for download at http://scholar.harvard.edu/gerrard/mastering-scientific-computation-r. This data is designed to represent the proportion of a solid surface to which gas is bound at 50 different pressure readings. It contains four variables:

    • P is the pressure at which measurements were taken

    • T1, T2, and T3 are the proportions of the solid covered in gas at temperatures 1, 2, and 3

  • Anthropometric data obtained from the Centers for Disease Control's National Health and Nutrition Examination Survey, 2011-2012, provided as a comma-separated values (CSV) file is available for download at http://scholar.harvard.edu/gerrard/mastering-scientific-computation-r. This dataset has been cleaned and processed for use and contains 8,602 individuals and four variables:

    • Gender (1 is for male, 2 is for female)

    • Age

    • Height

    • Weight

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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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