June 2016
Beginner to intermediate
398 pages
8h 51m
English
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The EM algorithm for the imputation of missing values
The EM algorithm is extensively used for the imputation of missing values. Implementations include (van Buuren and Groothuis-Oudshoorn 2011), (Schafer 1997), (Templ, Alfons, and Filzmoser 2011), (Raghunathan et al. 2001), and (Gelman and Hill 2011). In the following we want to show how an EM algorithm works generally for these kind of problems.
First we take a data set to impute. We select again the sleep data:
library("MASS") library("robustbase") library("VIM") data("sleep") str(sleep) ## 'data.frame': 62 obs. of 10 variables: ## $ BodyWgt : num 6654 1 3.38 0.92 2547 ... ## $ BrainWgt: num 5712 6.6 44.5 5.7 4603 ... ## $ NonD : num NA 6.3 NA NA 2.1 9.1 15.8 5.2 10.9 8.3 ... ## $ Dream : num ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.