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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

Descriptive statistics

A useful tool to evaluate your data before you begin your analysis is the summary() function, which provides a summary of the non-parametric descriptors of a sample, as follows:

> summary(probeA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
  4.211   4.645   4.774   4.774   4.892   5.231

You can also get a summary for each column of the matrix using the summary() function, as follows:

> summary(MLLpartner.mx) #output truncated
   GSM487973        GSM487972        GSM487971     
 Min.   : 2.112   Min.   : 1.805   Min.   : 1.994  
 1st Qu.: 3.412   1st Qu.: 3.410   1st Qu.: 3.411  
 Median : 4.736   Median : 4.745   Median : 4.731  
 Mean   : 5.342   Mean   : 5.346   Mean   : 5.355  
 3rd Qu.: 6.870   3rd Qu.: 6.851   3rd Qu.: 6.933  
 Max.   :14.449   Max.   :14.453   Max.   :14.406  

We can also get this information by calling the individual functions used to determine these parameters, namely the mean, median, min, max, and quantile functions, as follows:

&gt...
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Mastering Scientific Computing with R
Published in: Jan 2015
Publisher:
ISBN-13: 9781783555253
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