statsmodels.distributions.copula.api.CopulaDistribution.rvs¶

CopulaDistribution.rvs(nobs=1, cop_args=None, marg_args=None, random_state=None)[source]¶

Draw n in the half-open interval [0, 1).

Sample the joint distribution.

Parameters:¶

nobsint, optional

Number of samples to generate in the parameter space. Default is 1.

cop_argstuple

Copula parameters. If None, then the copula parameters will be taken from the cop_args attribute created when initiializing the instance.

marg_argslist of tuples

Parameters for the marginal distributions. It can be None if none of the marginal distributions have parameters, otherwise it needs to be a list of tuples with the same length has the number of marginal distributions. The list can contain empty tuples for marginal distributions that do not take parameter arguments.

random_state{None, int, numpy.random.Generator}, optional

If seed is None then the legacy singleton NumPy generator. This will change after 0.13 to use a fresh NumPy Generator, so you should explicitly pass a seeded Generator if you need reproducible results. If seed is an int, a new Generator instance is used, seeded with seed. If seed is already a Generator instance then that instance is used.

Returns:¶

samplearray_like (n, d)

Sample from the joint distribution.

See also

statsmodels.tools.rng_qrng.check_random_state

Notes

The random samples are generated by creating a sample with uniform margins from the copula, and using ppf to convert uniform margins to the one specified by the marginal distribution.