inequality.theil.TheilD#

class inequality.theil.TheilD(y, partition, column=None)[source]#

Decomposition of Theil’s T based on partitioning of observations into exhaustive and mutually exclusive groups.

Parameters:

ynumpy.array, pandas.Series, or pandas.DataFrame: An array in the shape \((n,t)\) or \((n,)\) with \(n\) taken as the observations across which inequality is calculated. If y is \((n,)\) then a scalar inequality value is determined. If y is \((n,t)\) then an array of inequality values are determined, one value for each column in y. For a pandas.DataFrame, pass column (a name or list of names) to select the values.
partitionarray_like or pandas.Series: An array in the shape \((n,)\) of elements indicating which partition each observation belongs to. These are assumed to be exhaustive.
columnstr or list of str, optional: Name(s) of the column(s) holding the values when y is a pandas.DataFrame.

Attributes:

Tnumpy.array: An array in the shape \((t,)\) or \((1,)\) containing the global inequality T.
bgnumpy.array: An array in the shape \((n,t)\) or \((n,)\) representing between group inequality.
wgnumpy.array: An array in the shape \((n,t)\) or \((n,)\) representing within group inequality.

Examples

>>> import libpysal
>>> import numpy
>>> from inequality.theil import TheilD

>>> f = libpysal.io.open(libpysal.examples.get_path('mexico.csv'))
>>> vnames = [f'pcgdp{dec}' for dec in range(1940, 2010, 10)]
>>> y = numpy.array([f.by_col[v] for v in vnames]).T
>>> regimes = numpy.array(f.by_col('hanson98'))
>>> theil_d = TheilD(y, regimes)

>>> theil_d.bg
array([0.0345889 , 0.02816853, 0.05260921, 0.05931219, 0.03205257,
       0.02963731, 0.03635872])

>>> theil_d.wg
array([0.17435454, 0.12405598, 0.0521202 , 0.04263506, 0.06354856,
       0.07547525, 0.0702496 ])

__init__(y, partition, column=None)[source]#

Methods

__init__(y, partition[, column])