statsmodels.distributions.mixture_rvs.MixtureDistribution.cdf

MixtureDistribution.cdf(x, prob, dist, kwargs=None)

cdf of a mixture of distributions.

Parameters:

xarray_like

Array containing locations where the CDF should be evaluated

probarray_like

Probability of sampling from each distribution in dist

distarray_like

An iterable of distributions objects from scipy.stats.

kwargstuple of dicts, optional

A tuple of dicts. Each dict in kwargs can have keys loc, scale, and args to be passed to the respective distribution in dist. If not provided, the distribution defaults are used.

Examples

Say we want 5000 random variables from mixture of normals with two distributions norm(-1,.5) and norm(1,.5) and we want to sample from the first with probability .75 and the second with probability .25.

>>> import numpy as np
>>> from scipy import stats
>>> from statsmodels.distributions.mixture_rvs import MixtureDistribution
>>> x = np.arange(-4.0, 4.0, 0.01)
>>> prob = [.75,.25]
>>> mixture = MixtureDistribution()
>>> Y = mixture.cdf(x, prob, dist=[stats.norm, stats.norm],
...                 kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))