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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

Other optimization packages

There are a lot more packages available to solve optimization problems in R. Here is a short table summary of the packages or functions that will help you pick the best package and/or function for your optimization problem:

 

Linear Objective

Quadratic Objective

Non-linear Objective

Heuristic Approachs

No contraints, Box constraints

  

optim optimize nlminb

DEoptim rgenoud NMOF

Linear constraints

 

quadprog LowRankQP

constrOptim

 

Linear, integer constraints

Boot lpSolve lpSolveAPI linprog limSolve

   

Quadratic constraints

 

Rcplex Rmosek

  

Semi-definite

Rcsdp

   

Non-linear constraints

  

Rsolnp

 

Note

You can also go to http://cran.rproject.org/web/views/Optimization.html to visit the Optimization and Mathematical Programming web page for a more complete and up-to-date list of the packages available to solve optimization problems.

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Programming languages
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