inequality.theil.TheilDSim#

class inequality.theil.TheilDSim(y, partition, permutations=99, column=None)[source]#

Random permutation based inference on Theil’s inequality decomposition. Provides for computationally based inference regarding the inequality decomposition using random spatial permutations. See [RSastreGutierrez10].

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.
permutationsint: The number of random spatial permutations for computationally based inference on the decomposition.
columnstr or list of str, optional: Name(s) of the column(s) holding the values when y is a pandas.DataFrame.

Attributes:

observednumpy.array: An array in the shape \((n,t)\) or \((n,)\) representing a TheilD instance for the observed data.
bgnumpy.array: An array in the shape (permutations+1, t) representing between group inequality.
bg_pvaluenumpy.array: An array in the shape \((t,1)\) representing the \(p\)-value for the between group measure. Measures the percentage of the realized values that were greater than or equal to the observed bg value. Includes the observed value.
wgnumpy.array: An array in the shape (permutations+1) representing within group inequality. Depending on the shape of y, the array may be 1- or 2-dimensional.

Examples

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

>>> 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'))
>>> numpy.random.seed(10)
>>> theil_ds = TheilDSim(y, regimes, 999)

>>> theil_ds.bg_pvalue
array([0.4  , 0.344, 0.001, 0.001, 0.034, 0.072, 0.032])

__init__(y, partition, permutations=99, column=None)[source]#

Methods

__init__(y, partition[, permutations, column])