I am running a binary logistic regression with compositional predictors that sum to 100% (demographic categories). I've looked at several postings about this, but can't find a good solution to my problem. Would dropping a single predictor be useful in cases where 0% of the data comes from that category? I.e., if my predictors are race, and I drop "Hispanic/Latino", the hispanic/latino rate in my data ranges from 0% to 6% in each of my cases, so in many/most cases the data is still correlated.
Would a transformation be appropriate here?
I do have the ability to calculate a (rough) number for each category, since I do have the total number of individuals in each case, but I am more interested in effect of the proportion of the racial categories on my independent variable.
I've found these, but they don't present a solution.