Mastering scientific computing with R

Mastering scientific computing with R / Paul Gerrard, Radia M. Johnson.

"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...

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Bibliographic Details
Main Authors: Gerrard, Paul (Author), Johnson, Radia M. (Author)
Format: eBook
Language:English
Published: Birmingham, England : Packt Publishing, 2015.
Subjects:
R (Computer program language)
Science > Data processing.
Open source software.
MATHEMATICS > Applied.
MATHEMATICS > Probability & Statistics > General.
Open source software
Science > Data processing
Online Access:Click for online access
Table of Contents:
  • Cover
  • Copyright
  • Credits
  • About the Authors
  • About the Reviewers
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: Programming with R
  • Data structures in R
  • Atomic vectors
  • Operations on vectors
  • Lists
  • Attributes
  • Factors
  • Multidimensional arrays
  • Matrices
  • Data frames
  • Loading data into R
  • Saving data frames
  • Basic plots and the ggplot2 package
  • Flow control
  • The for() loop
  • The apply() function
  • The if() statement
  • The while() loop
  • The repeat{} and break statement
  • FunctionsGeneral programming and debugging tools
  • Summary
  • Chapter 2: Statistical Methods with R
  • Descriptive statistics
  • Data variability
  • Confidence intervals
  • Probability distributions
  • Fitting distributions
  • Higher order moments of a distribution
  • Other statistical tests to fit distributions
  • The propagate package
  • Hypothesis testing
  • Proportion tests
  • Two sample hypothesis tests
  • Unit root tests
  • Summary
  • Chapter 3: Linear Models
  • An overview of statistical modeling
  • Model formulas
  • Explanatory variables interactionsError terms
  • The intercept as parameter 1
  • Updating a model
  • Linear regression
  • Plotting a slope
  • 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
  • Polynomial regressionPerforming a polynomial regression in R
  • Spline regression
  • Nonparametric nonlinear methods
  • Kernel regression
  • Kernel weighted local polynomial fitting
  • Optimal bandwidth selection
  • A practical scientific application of kernel regression
  • Locally weighted polynomial regression and the loess function
  • Nonparametric methods with the np package
  • Nonlinear quantile regression
  • Summary
  • Chapter 5: Linear Algebra
  • Matrices and linear algebra
  • Matrices in R
  • Vectors in R
  • Matrix notation
  • The physical functioning datasetBasic matrix operations
  • Element-wise matrix operations
  • Matrix subtraction
  • Matrix addition
  • Matrix sweep
  • Basic matrix-wise operations
  • Transposition
  • Matrix multiplication
  • Matrix inversion
  • Determinants
  • Triangular matrices
  • Matrix decomposition
  • QR decomposition
  • Eigenvalue decomposition
  • Lower upper decomposition
  • Cholesky decomposition
  • Singular value decomposition
  • Applications
  • Rasch analysis using linear algebra and a paired comparisons matrix

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