I have done some surfing to find an answer but I am still stuck. I have a dataset with n rows and p columns. I have several missing data across most of the variables going from 2% up to 70%. I am aware of several techniques to impute them, but I cannot figure out what is the amount of missing data per variable for which it is safe to run the imputation, with whatever algorithm.
For instance, I found this post on r-bloggers which is basically a tutorial for the well known R's package mice. When introducing the concept of Missing At Random (MAR) and Missing Completely At Random (MCAR), the author says:
Assuming data is MCAR, too much missing data can be a problem too. Usually a safe maximum threshold is 5% of the total for large datasets. If missing data for a certain feature or sample is more than 5% then you probably should leave that feature or sample out.
Now, I am assuming my data to be MAR, so it is slightly different from the case above, but still the question holds. If I trust the guideline here I can forget to impute most of my variables.
Do you have any hints or suggestions whatsoever?
Thank you in advance,