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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Table of Contents (15) Chapters Close

Preface 1. Practical Machine Learning with R FREE CHAPTER 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Fitting multiple regression


Multiple regression includes multiple predictors and using the same lm function we can create a model.

Getting ready

You have completed the previous recipes and have installed the car package. You are also aware of using the lm function. In this recipe, we will use the Prestige dataset from the car package.

How to do it...

Perform the following step to perform multiple regression with lm:

> head(Prestige)
Output:
 education income women prestige census type
gov.administrators 13.11 12351 11.16 68.8 1113 prof
general.managers 12.26 25879 4.02 69.1 1130 prof
accountants 12.77 9271 15.70 63.4 1171 prof
purchasing.officers 11.42 8865 9.11 56.8 1175 prof
chemists 14.62 8403 11.68 73.5 2111 prof
physicists 15.64 11030 5.13 77.6 2113 prof
> str(Prestige)
Output
'data.frame': 102 obs. of 6 variables:
 $ education: num 13.1 12.3 12.8 11.4 14.6 ...
 $ income : int 12351 25879 9271 8865 8403 11030 8258 14163 11377 11023 ...
 $ women : num 11.16 4.02 15.7 9.11 11.68 .....
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