copularsimulation
So I have got a data set with 3 variables. Using the function C.n(u,pobs(data)), where u is a matrix containing uniform distributed random variables of dimension 3 and the same length as the data set.
However, C.n returns a list of numbers in [0,1] of only one dimension. How can I use this list to generate new data, in three dimensions?
Check out A Short, Comprehensive, Practical Guide to Copulas by Atillio Meucci. The paper provides further references in case you'd like to learn more. Steps #3 and #5 are addressed by this paper.
Step #4 is specific to this particular function which I am not too familiar. Since you have so many questions you'd probably have better success breaking down the problem into related parts. You will be in a better place to frame question #4 after some further background reading. As such you really can't answer the question without going into the background of copulas.
There are parametric and non-parametric methods to estimating copulas. In my view, parametric copulas impose tight restrictions that are not respected by the data (non-stationarity, fat tails, etc.). More recent research on time-varying copulas and Meucci's non-parametric copulas I believe can better cope with these issues.
Thanks for your answers. Indeed, when I plot the different Copulas, it seems (based on a visual comparison) that the dependence struture is depicted in the copula.
Then my next step will be to compare the different copula families in terms of looking at their goodness of fit.
@Ben: Ok, so if the distribution of one of my marginals is the generalized extreme value distribution, my u or v will be passed to the "x" of this function, since the other parameters of this distribution are fitted before. 
Best Answer
Looking at the copula package's documentation, there doesn't seem to be a way to generate samples from empirical copulas. The R function that is used typically for this purpose is rCopula(), which requires a copula object datatype.
The following paper describes how to generate random samples from empirical copulas: http://www.informs-sim.org/wsc07papers/058.pdf, and this in-fact requires the empirical copula density.
If you want to stick w/ R, then I suppose you have two options:
If you are open to using other software packages, I have written some tools in Matlab to do this (they might work in Octave also, but I haven't tested that functionality). All the code lives in this toolbox here: Copula Matlab Toolbox
Specifically, the function you will need to call to generate random pseudo-observations from an empirical copula is empcopularnd. This function implements the method described in the paper above, and requires an empirical copula density as it's input. To generate empirical copula densities, you can use the function empcopulapdf. An example of how to put all of this together (generate empirical density and draw pseudo-samples from the density) is in the test-script test_empcopularnd.
The functions written above should work for any dimensionality, but obviously as your data grows in dimensionality the code will be slower. To speed it up, you may consider compiling the mex file empcopulapdf_c. If you do end up using this code, please PM me if you have any questions!