R/firm-clustering.R
grouping.classify.Rdclusters firms based on their cross-sectional wage distributions
grouping.classify(measures, ksupp = ceiling((1:(60)^(1/1.3))^1.3), nstart = 1000, iter.max = 200, stop = FALSE, verbose = 1, cval = 1)
| measures | specify the type of measures to use (mean and var, quantiles, etc...) |
|---|---|
| ksupp | vector of different number of groups to try |
| nstart | (default:1000) total number of starting values |
| iter.max | (default:100) max nunmber of step for each repetition |
| sdata | cross sectional data, needs a column j (firm id) and w (log wage) |
| Nw | number of points to use for wage distributionsdsd |
| M | you can pass the matrix measurements, requires also weights W (pass on the truth for instance) |