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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

Analysis of variance

Analysis of variance (Anova) is used to fit data to a linear model when all explanatory variables are categorical. Each of these categorical explanatory variables is known as a factor, and each factor can have two or more levels. When a single factor is present with three or more levels, we use a one-way Anova to analyze the data. If we had a single factor with two levels, we would use a student's t-test to analyze the data. When there are two or more factors, we would use a two-way or three-way Anova. You can easily perform an Anova in R using the aov() function.

In the first example, we will look at the effect of the dose of drugA on the level of fatigue reported by the 20 patients as follows:

> patient.fatigue <- read.table(header=TRUE, text='
   patients fatigue drugA_dose
1         1     low        0.2
2         2     low        0.2
3         3     med        0.2
4         4     med        0.2
5         5     med        0.2
6         6     low   ...
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