Consider a sparse dataframe with lots of NA and a large number of explanatory variables and a response variable. The response variable doesn't contain any NA.
I want to clean this dataframe by discarding some of the explanatory variables having most of their entries NAs.
Is there any study that suggests the minimum percentage of non-NA values that must be present in a variable to qualify it as a predictor? For example, if the total percentage of non-NA values in column X is less than 5% of total records, then drop column X.