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Mastering Scientific Computing with R

You're reading from   Mastering Scientific Computing with R Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783555253
Length 432 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Programming with R 2. Statistical Methods with R FREE CHAPTER 3. Linear Models 4. Nonlinear Methods 5. Linear Algebra 6. Principal Component Analysis and the Common Factor Model 7. Structural Equation Modeling and Confirmatory Factor Analysis 8. Simulations 9. Optimization 10. Advanced Data Management Index

Simulating physical systems


As a brief introduction to simulating physical systems, we will show you how to simulate Brownian motion in R. In physics, Brownian motion is defined as the random movement of particles suspended in liquid or gas caused by the collision of these particles in its surrounding medium. As a result, Brownian motion can be seen as a stochastic process continuous in time. We can simulate this process by successively adding random variables from a normal distribution, where the total number of normal random variables to be simulated represents the total number of discrete time intervals. For example, let's plot Brownian motion in one dimension using 10,000 discrete time intervals as follows:

> motion <- rnorm(10000, 0, 1)
> motion <- cumsum(motion)

> plot(motion, type="l", main="Brownian Motion in 1-Dimension", xlab="time", ylab="displacement")

The result is shown in the following plot:

Alternatively, we could plot simple Brownian motion in two dimensions...

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