Copula generation (Gaussian, t and Gumbel) with the help of correlation matrix using R I have a set of data of 2 variates. I have generated correlation matrix between the variates. Using copula package of R, I computed t-copula using correlation matrix. I used the following technique for that:


*

*With the help of correlation matrix for example let it be corr_mat, I computed parameter vector, lets say param_vect.

*Then I computed tcopula object, let it be t_object, using
tCopula(param_vect, dim =..., dispstr="un", df =...)

*Lastly I simulated 1000 sample from the copula using rcopula(1000,t_object).
The above procedure works fine for t-copula. Can someone please suggest me amethod to simulate samples from the Gaussian and Gumbell copulas? I  tried with the gumbelcopula function, but it fails with errors. 
Could anyone one suggest the method to simulate copula using correlation matrix?
 A: Correlation is problematic because it depends on the marginal distribution. 
Change the margins (which doesn't alter the copula) and the correlation changes. A specific correlation is not associated with a given copula and vice-versa. 
The correlation matrix that is an input to a t-copula doesn't define the correlation of the resulting variables that have that copula, so you may not be achieving what you think you are when you use the t-copula function. Similarly with the correlation in a Gaussian copula - you can specify the value of the parameter $\rho$, but that doesn't mean that the correlation of the variables you generate with that Gaussian copula is $\rho$. If they also have Gaussian margins, then the correlation is $\rho$, otherwise it will generally not be $\rho$, but some function of it that depends on what the margins are.
This is why many people focus on nonparametric correlations like Kendall's tau or Spearman's rho. Those are specified by a given copula, and apply to any (continuous) margin.
For a number of specific copula families there are often relatively simple relationships between the parameters of the family and one or both of those common measures of nonparametric correlation.
