We need to know that data does not have negative values in it -- or at least that the mean() of each column is definitely positive.
If there are any columns with mean() that is negative, then min(M) is going to be negative, and negative times 1.1 is more negative, which is probably not what you want.
If not then if there are any columns of data that are all-zero, then their mean() is 0, and 0*1.1 is 0, which is probably not what you want.
I am trying to work out of your B.*data can zero out a column "accidentally", knocking out positive values but leaving zeros. I believe the answer to that is NO. But it is possible for an entire non-zero column to be knocked out. For example if the column were all 50, mean() of it would be 50, that might happen to be the min(), threshold would then be 55, but 50 < 55 in all positions, so the column could get knocked out.
... A bunch of this is trying to figure out the consequences if there are negative values or 0 already in the data, which is something we are not told.
