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Simulation for Data Science with R

You're reading from   Simulation for Data Science with R Effective Data-driven Decision Making

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781785881169
Length 398 pages
Edition 1st Edition
Languages
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Author (1):
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Matthias Templ Matthias Templ
Author Profile Icon Matthias Templ
Matthias Templ
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Table of Contents (13) Chapters Close

Preface 1. Introduction 2. R and High-Performance Computing FREE CHAPTER 3. The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions 4. Simulation of Random Numbers 5. Monte Carlo Methods for Optimization Problems 6. Probability Theory Shown by Simulation 7. Resampling Methods 8. Applications of Resampling Methods and Monte Carlo Tests 9. The EM Algorithm 10. Simulation with Complex Data 11. System Dynamics and Agent-Based Models Index

Generic functions, methods, and classes

R has different class systems, the most important ones are S3 and S4 classes. Programming with S3 classes is easy living, it's easier than S4. However, S4 is clean and the use of S4 can make packages very user-friendly.

In any case, in R each object is assigned to a class (the attribute class). Classes allow object-oriented programming and overloading of generic functions. Generic functions produce different output for objects of different classes as soon as methods are written for such classes.

This sounds complex, but with the following example it should get clearer.

As an example of a generic function, we will use the function summary. summary is a generic function used to produce result summaries. The function invokes particular methods that depend on the class of the first argument:

## how often summary is overloaded with methods
## on summary for certain classes
(the number depends on loaded packages)
length(methods(summary))
## [1] 137
class...
You have been reading a chapter from
Simulation for Data Science with R
Published in: Jun 2016
Publisher: Packt
ISBN-13: 9781785881169
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