Introduction to scientific programming and simulation using R /
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| Author / Creator: | Jones, Owen (Owen Dafydd) |
|---|---|
| Imprint: | Boca Raton, FL : CRC Press, c2009. |
| Description: | xix, 453 p. : ill., charts ; 25 cm. |
| Language: | English |
| Series: | Chapman & Hall book |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/7798353 |
Table of Contents:
- Preface
- I. Programming
- 1. Setting up
- 1.1. Installing R
- 1.2. Starting R
- 1.3. Working directory
- 1.4. Writing scripts
- 1.5. Help
- 1.6. Supporting material
- 2. R as a calculating environment
- 2.1. Arithmetic
- 2.2. Variables
- 2.3. Functions
- 2.4. Vectors
- 2.5. Missing data
- 2.6. Expressions and assignments
- 2.7. Logical expressions
- 2.8. Matrices
- 2.9. The workspace
- 2.10. Exercises
- 3. Basic programming
- 3.1. Introduction
- 3.2. Branching with if
- 3.3. Looping with for
- 3.4. Looping with while
- 3.5. Vector-based programming
- 3.6. Program flow
- 3.7. Basic debugging
- 3.8. Good programming habits
- 3.9. Exercises
- 4. I/O: Input and Output
- 4.1. Text
- 4.2. Input from a file
- 4.3. Input from the keyboard
- 4.4. Output to a file
- 4.5. Plotting
- 4.6. Exercises
- 5. Programming with functions
- 5.1. Functions
- 5.2. Scope and its consequences
- 5.3. Optional arguments and default values
- 5.4. Vector-based programming using functions
- 5.5. Recursive programming
- 5.6. Debugging functions
- 5.7. Exercises
- 6. Sophisticated data structures
- 6.1. Factors
- 6.2. Dataframes
- 6.3. Lists
- 6.4. The apply family
- 6.5. Exercises
- 7. Better graphics
- 7.1. Introduction
- 7.2. Graphics parameters: par
- 7.3. Graphical augmentation
- 7.4. Mathematical typesetting
- 7.5. Permanence
- 7.6. Grouped graphs: lattice
- 7.7. 3D-plots
- 7.8. Exercises
- 8. Pointers to further programming techniques
- 8.1. Packages
- 8.2. Frames and environments
- 8.3. Debugging again
- 8.4. Object-oriented programming: S3
- 8.5. Object-oriented programming: S4
- 8.6. Compiled code
- 8.7. Further reading
- 8.8. Exercises
- II. Numerical techniques
- 9. Numerical accuracy and program efficiency
- 9.1. Machine representation of numbers
- 9.2. Significant digits
- 9.3. Time
- 9.4. Loops versus vectors
- 9.5. Memory
- 9.6. Caveat
- 9.7. Exercises
- 10. Root-finding
- 10.1. Introduction
- 10.2. Fixed-point iteration
- 10.3. The Newton-Raphson method
- 10.4. The secant method
- 10.5. The bisection method
- 10.6. Exercises
- 11. Numerical integration
- 11.1. Trapezoidal rule
- 11.2. Simpson's rule
- 11.3. Adaptive quadrature
- 11.4. Exercises
- 12. Optimisation
- 12.1. Newton's method for optimisation
- 12.2. The golden-section method
- 12.3. Multivariate optimisation
- 12.4. Steepest ascent
- 12.5. Newton's method in higher dimensions
- 12.6. Optimisation in R and the wider world
- 12.7. A curve fitting example
- 12.8. Exercises
- III. Probability and statistics
- 13. Probability
- 13.1. The probability axioms
- 13.2. Conditional probability
- 13.3. Independence
- 13.4. The Law of Total Probability
- 13.5. Bayes' theorem
- 13.6. Exercises
- 14. Random variables
- 14.1. Definition and distribution function
- 14.2. Discrete and continuous random variables
- 14.3. Empirical cdf's and histograms
- 14.4. Expectation and finite approximations
- 14.5. Transformations
- 14.6. Variance and standard deviation
- 14.7. The Weak Law of Large Numbers
- 14.8. Exercises
- 15. Discrete random variables
- 15.1. Discrete random variables in R
- 15.2. Bernoulli distribution
- 15.3. Binomial distribution
- 15.4. Geometric distribution
- 15.5. Negative binomial distribution
- 15.6. Poisson distribution
- 15.7. Exercises
- 16. Continuous random variables
- 16.1. Continuous random variables in R
- 16.2. Uniform distribution
- 16.3. Lifetime models: exponential and Weibull
- 16.4. The Poisson process and the gamma distribution
- 16.5. Sampling distributions: normal, X2, and t
- 16.6. Exercises
- 17. Parameter Estimation
- 17.1. Point Estimation
- 17.2. The Central Limit Theorem
- 17.3. Confidence intervals
- 17.4. Monte-Carlo confidence intervals
- 17.5. Exercises
- IV. Simulation
- 18. Simulation
- 18.1. Simulating iid uniform samples
- 18.2. Simulating discrete random variables
- 18.3. Inversion method for continuous rv
- 18.4. Rejection method for continuous rv
- 18.5. Simulating normals
- 18.6. Exercises
- 19. Monte-Carlo integration
- 19.1. Hit-and-miss method
- 19.2. (Improved) Monte-Carlo integration
- 19.3. Exercises
- 20. Variance reduction
- 20.1. Antithetic sampling
- 20.2. Importance sampling
- 20.3. Control variates
- 20.4. Exercises
- 21. Case studies
- 21.1. Introduction
- 21.2. Epidemics
- 21.3. Inventory
- 21.4. Seed dispersal
- 22. Student projects
- 22.1. The level of a dam
- 22.2. Roulette
- 22.3. Buffon's needle and cross
- 22.4. Insurance risk
- 22.5. Squash
- 22.6. Stock prices
- Glossary of R commands
- Programs and functions developed in the text
- Index
