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