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
k	number of groups
nstart	(default:1000) total number of starting values
iter.max	(default:100) max number of step for each repetition
step	step size in the repeating
sdata	cross sectional data, needs a column j (firm id) and w (log wage)
Nw	number of points to use for wage distribution