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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
Languages
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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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Toc

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)

Loading and observing data


In this recipe, we will see how to load and view survival data. We will create a response object to fit in the survival model.

Getting ready

This recipe requires survival package, as we will be using a dataset from it.

How to do it...

Perform the following steps to perform survival analysis:

  1. You should install the survival package and load its library:
> install.packages("survival")> library(survival)
  1. From the package, you can load the cancer dataset:
        > data(cancer)  
  1. You can use the str function to display the structure of the cancer dataset:
        > str(cancer)
        Output  
        'data.frame':    228 obs. of  10 variables:
         $ inst     : num  3 3 3 5 1 12 7 11 1 7 ...
         $ time     : num  306 455 1010 210 883 ...
         $ status   : num  2 2 1 2 2 1 2 2 2 2 ...
         $ age      : num  74 68 56 57 60 74 68 71 53 61 ...
         $ sex      : num  1 1 1 1 1 1 2 2 1 1 ...
         $ ph.ecog  : num  1 0 0 1 0 1 2 2 1 2 ...
    ...
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