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Mastering Scientific Computing with R by Radia M. Johnson, Paul Gerrard

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Missing data

One of the biggest problems in real-world data is missing data. In carefully planned experiments on inanimate chemicals, small samples of rats, or highly mechanized factories, missing data may not be such a problem. However, whenever a dataset gets large enough, or starts to involve humans, missing data is almost a certainty. Let's begin by pointing out that if you have missing data, then you have a missing data problem, and you have to do something with that missing data; the question is, what? The answer lies in what kind of bias you are dealing with as a result of missing data.

Computational aspects of missing data in R

Before we delve into the statistical aspects of missing data, we need to review the computational ones. There are ...

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