Currently I am working on a large data set with well over 200 variables (238 to be exact) and 290 observations for each variable (in theory). This data set is missing quite a lot of values, with variables ranging from 0-100% 'missingness'. I will eventually be performing logistical regression on this data, so of my 238 columns I will at most only be using ten or so.
However as almost all of my columns are missing some data, I am turning to multiple imputation to fill in the blanks (using the MICE package).
My question is; given that I have a large amount of variation in the missing data, at what percentage missing should I start to exclude variables from the mice() function?
Can mice function well with variables that are missing 50% of their values? What about 60%, 70%, 80%, 90%?