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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

Matrices and linear algebra


In mathematics, matrices are simply an organized table of numbers. The reason they are used is because scientists and mathematicians have found that some numerical problems (generally systems of equations) can be solved algorithmically when the data placed into the problem is organized this way. In mathematics, a row or column of a matrix is termed a "vector", though R has a vector data structure that is not the same thing as the row or column of a matrix. The numbers characterizing a vector in mathematics are thought of as the endpoint of that vector with vector's origin at the origin of the coordinate system (this is what we term a "vector" mathematically, but it is really a point).

While the term "matrix" is often used to refer to a rectangular arrangement for string data (that is, a table), we must be much more cautious with our use of the term in numeric computation since R has more than one way to store composite variables.

Matrices in R

While data analysts...

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