Mastering Scientific Computing with R

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

Mastering Scientific Computing with R

Paul Gerrard, Radia M. Johnson

1 customer reviews
Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment
Mapt Subscription
FREE
$29.99/m after trial
eBook
$21.00
RRP $29.99
Save 29%
Print + eBook
$49.99
RRP $49.99
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$21.00
$49.99
$29.99p/m after trial
RRP $29.99
RRP $49.99
Subscription
eBook
Print + eBook
Start 30 Day Trial

Frequently bought together


Mastering Scientific Computing with R Book Cover
Mastering Scientific Computing with R
$ 29.99
$ 21.00
Numerical and Scientific Computing with SciPy [Video] Book Cover
Numerical and Scientific Computing with SciPy [Video]
$ 124.99
$ 106.25
Buy 2 for $35.00
Save $119.98
Add to Cart
Subscribe and access every Packt eBook & Video.
 
  • 5,000+ eBooks & Videos
  • 50+ New titles a month
  • 1 Free eBook/Video to keep every month
Start Free Trial
 

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
Read More
From 1 reviews

Read More Reviews

Recommended for You

Practical Data Science Cookbook Book Cover
Practical Data Science Cookbook
$ 29.99
$ 21.00
Machine Learning with R Book Cover
Machine Learning with R
$ 32.99
$ 23.10
R for Data Science Book Cover
R for Data Science
$ 29.99
$ 21.00
Practical Data Analysis Book Cover
Practical Data Analysis
$ 29.99
$ 21.00
Python Data Analysis Book Cover
Python Data Analysis
$ 29.99
$ 21.00
Learning Bayesian Models with R Book Cover
Learning Bayesian Models with R
$ 35.99
$ 25.20