statsmodels.distributions.copula.api.CopulaDistribution.rvs

CopulaDistribution.rvs(nobs=1, cop_args=None, marg_args=None, random_state=None)

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

Sample the joint distribution.

Parameters:

• nobs (int, optional) – Number of samples to generate in the parameter space. Default is 1.

• cop_args (tuple) – Copula parameters. If None, then the copula parameters will be taken from the cop_args attribute created when initiializing the instance.

• marg_args (list 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:

sample – Sample from the joint distribution.

Return type:

array_like (n, d)

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.

See also

statsmodels.tools.rng_qrng.check_random_state