clusters firms based on their cross-sectional wage distributions

grouping.classify.once(measures, k = 10, nstart = 1000, iter.max = 200,
step = 20)

## Arguments

measures object created using grouping.getMeasures number of groups (default:1000) total number of starting values (default:100) max number of step for each repetition step size in the repeating cross sectional data, needs a column j (firm id) and w (log wage) number of points to use for wage distribution