Here you will find daily news and tutorials about R, contributed by over 573 bloggers.
There are many ways to follow us - By e-mail:On Facebook: If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

(This article was first published on is.R(), and kindly contributed to R-bloggers)

So, what if you have data, but some of the observations are missing? Many statistical techniques assume no missingness, so we might want to “fill in” or rectangularize our data, by replacing missing observations with plausible substitutes. There are many ways of going about this, but one of the most robust and accessible is through the Amelia package.

Today’s Gist applies multiple imputation to some sample ANES survey data, and compares listwise-deleted regression results to results pooled from the same regression run on ten imputed data sets. Amelia makes this imputation, modeling, and recombination straightforward, and I’ve thrown in a nice coefficient plot (using position_dodge!) to illustrate the differences between missing data approaches.