statsmodels.distributions.empirical_distribution.ECDFDiscrete¶

class statsmodels.distributions.empirical_distribution.ECDFDiscrete(x, freq_weights=None, side='right')[source]¶

Return the Empirical Weighted CDF of an array as a step function.

Parameters:¶

xarray_like

Data values. If freq_weights is None, then x is treated as observations and the ecdf is computed from the frequency counts of unique values using nunpy.unique. If freq_weights is not None, then x will be taken as the support of the mass point distribution with freq_weights as counts for x values. The x values can be arbitrary sortable values and need not be integers.

freq_weightsarray_like

Weights of the observations. sum(freq_weights) is interpreted as nobs for confint. If freq_weights is None, then the frequency counts for unique values will be computed from the data x.

side{‘left’, ‘right’}, optional

Default is ‘right’. Defines the shape of the intervals constituting the steps. ‘right’ correspond to [a, b) intervals and ‘left’ to (a, b].

Methods

__call__(time)

Call self as a function.

Returns:¶

Weighted ECDF as a step function.

Examples

>>> import numpy as np
>>> from statsmodels.distributions.empirical_distribution import (
>>>     ECDFDiscrete)
>>>
>>> ewcdf = ECDFDiscrete([3, 3, 1, 4])
>>> ewcdf([3, 55, 0.5, 1.5])
array([0.75, 1.  , 0.  , 0.25])
>>>
>>> ewcdf = ECDFDiscrete([3, 1, 4], [1.25, 2.5, 5])
>>>
>>> ewcdf([3, 55, 0.5, 1.5])
array([0.42857143, 1., 0. , 0.28571429])
>>> print('e1 and e2 are equivalent ways of defining the same ECDF')
e1 and e2 are equivalent ways of defining the same ECDF
>>> e1 = ECDFDiscrete([3.5, 3.5, 1.5, 1, 4])
>>> e2 = ECDFDiscrete([3.5, 1.5, 1, 4], freq_weights=[2, 1, 1, 1])
>>> print(e1.x, e2.x)
[-inf  1.   1.5  3.5  4. ] [-inf  1.   1.5  3.5  4. ]
>>> print(e1.y, e2.y)
[0.  0.2 0.4 0.8 1. ] [0.  0.2 0.4 0.8 1. ]

Methods