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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

Pseudorandom numbers


Now, we will show you how to simulate random numbers, or more accurately called pseudorandom numbers, because as you will see, unlike true random numbers, which truly can't be predicted, these numbers can be predicted using random number generator algorithms. Let's review how to generate a series of pseudorandom numbers from 0 to 1. One of the simplest methods is to simulate independent uniform random variables using a multiplicative congruential pseudorandom number generator. Since there are many different methods used to generate pseudorandom numbers, let's consider the following example of a simple multiplicative congruential pseudorandom number generator that can be used to simulate random variables. Let m be a large prime integer and k be another integer less than 10, preferably close to the square root of m. We can generate pseudorandom numbers by applying the following formula:

In the preceding formula, i = 0, 1, 2, 3, … , n.

Now let's apply the previous formula...

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