I have a large dataset (501 rows and 39 columns) with a lot of missing data. I have already deleted all the rows where the (binary) response variable is missing as well as three columns that were variables clearly not helpful in the logistic regression (they were year, month of admission and race for predicting viral vs bacterial desease).
But there are still many, many, missing values. I don't need to impute values in a very complex way. If there are less than 15%
NA's I can just set them to the mean of the given column. But there are still only 3 variables with between 16 and 20%
NA's and the rest has more than that.
So I removed all rows with
NA's from a specific variable $X$ and that produced a set with 19 variables with less then 15%
NA's. But I probably could have selected $X$ in a way that would make more sense than just randomly selecting it.
Then I could do the same thing with a different variable and compare the two models later.
If there is a simple procedure for dealing with this kind of dataset I would appreciate if someone could point me towards it.