My question in short: are there methods to improve on the running time of R MICE (data imputation)?
I'm working with a data set (30 variables, 1.3 million rows) which contains (quite randomly) missing data. About 8% of the observations in about 15 out of 30 variables contain NAs. In order to impute the missing data, I'm running the MICE function, part of the MICE package.
I experience quite slow running time, even on a subset (100,000 rows), with method="fastpmm" and m=1 and runs for about 15 minutes.
Is there a way to improve on running time without losing too much in performance? (mice.impute.mean is quite fast, but comes with important loss of information!).
library(mice) df <- data.frame(replicate(30,sample(c(NA,1:10),1000000,rep=TRUE))) df <- data.frame(scale(df)) output <- mice(df, m=1, method = "fastpmm")