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Statistical Application Development with R and Python - Second Edition

You're reading from  Statistical Application Development with R and Python - Second Edition

Product type Book
Published in Aug 2017
Publisher
ISBN-13 9781788621199
Pages 432 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (19) Chapters close

Statistical Application Development with R and Python - Second Edition
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Data Characteristics 2. Import/Export Data 3. Data Visualization 4. Exploratory Analysis 5. Statistical Inference 6. Linear Regression Analysis 7. Logistic Regression Model 8. Regression Models with Regularization 9. Classification and Regression Trees 10. CART and Beyond Index

Model validation and diagnostics


In the previous chapter, we saw the utility of residual techniques. A similar technique is also required for the logistic regression model and we will develop these methods for the logistic regression model in this section.

Residual plots for the GLM

In the case of linear regression model, we had explored the role of residuals for the purpose of model validation. In the context of logistic regression, actually GLM, we have five different types of residuals for the same purpose:

  • Response residual: The difference between the actual values and the fitted values is the response residual, that is, , and in particular it is if yi = 1 and for yi = 0.
  • Deviance residual: For an observation i, the deviance residual is the signed square root of the contribution of the observation to the sum of the model deviance. That is, it is given by:

Where the sign is positive if , and negative otherwise, and is the predicted probability of success.

  • Pearson residual: The Pearson...

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