Dear all,

I just came to R a few days ago. Now I have a problem that I have two
correlated variables and want to first fit a Gaussian copula, then sample
it to generate simulated variables. I have spent last two days looking at R
archive and copula help file but couldn't find what I need. If my
understanding is correct, all examples I saw work in this way: a man-made
copula -> simulated data -> fit the same copula. But what I need to do is:
real data (bivariant) -> fit a Gaussian copula -> simulate variants from
this copula -> transform variants into marginal distribution -> using
inverse CDF to generate new data (variants) . Suppose I have two continuous
variables N and S for 263 companies, N is the number of subsidiaries, and S
is size. How can I do it?  Thank you in advance if you may show me some
sample codes.

-- 
Greetings,

Xiaoheng Zhang (Kevin)
Department of Economics
Trinity College Dublin
Republic of Ireland

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