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R in Action by Robert Kabacoff

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Chapter 15. Advanced methods for missing data

 

This chapter covers
  • Identification of missing data
  • Visualization of missing data patterns
  • Complete-case analysis
  • Multiple imputation of missing data

 

In previous chapters, we focused on the analysis of complete datasets (that is, data-sets without missing values). Although doing so has helped simplify the presentation of statistical and graphical methods, in the real world, missing data are ubiquitous.

In some ways, the impact of missing data is a subject that most of us want to avoid. Statistics books may not mention it or may limit discussion to a few paragraphs. Statistical packages offer automatic handling of missing data using methods that may not be optimal. Even though most data analyses ...

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