I am planning to carry out multiple imputation on a large data set with around 4,000,000 observations and 32 variables and 10 interactions. Of these variables six have missing data which I need to impute.
I plan to use MICE package from R and the fully conditionally specified method, outlined here: https://www.jstatsoft.org/article/view/v045i03
I have tried to run a test imputation using a subset of 10,000 observations, for 1 iteration and 5 imputation data sets and it is taking very long to run, so I am worried how long it will take when I try it with the full data set. I have spent quite a lot of time properly understanding how this package works and how to parallelise the different imputations across cores, so I would like to use this package if it is feasible.
My questions are:
1) How large a data set can MICE handle?
2) Does any body have experience imputing large data sets using the MICE package, if so how large was it and how long did it take to run?
3) Are there any other packages which do the same thing but designed specifically to handle large data set?