pysal.explore.esda.Moran_Local_BV

class pysal.explore.esda.Moran_Local_BV(x, y, w, transformation='r', permutations=999, geoda_quads=False)[source]

Bivariate Local Moran Statistics

Parameters:
x : array

x-axis variable

y : array

(n,1), wy will be on y axis

w : W

weight instance assumed to be aligned with y

transformation : {‘R’, ‘B’, ‘D’, ‘U’, ‘V’}

weights transformation, default is row-standardized “r”. Other options include “B”: binary, “D”: doubly-standardized, “U”: untransformed (general weights), “V”: variance-stabilizing.

permutations : int

number of random permutations for calculation of pseudo p_values

geoda_quads : boolean

(default=False) If True use GeoDa scheme: HH=1, LL=2, LH=3, HL=4 If False use PySAL Scheme: HH=1, LH=2, LL=3, HL=4

Examples

>>> import pysal.lib
>>> import numpy as np
>>> np.random.seed(10)
>>> w = pysal.lib.io.open(pysal.lib.examples.get_path("sids2.gal")).read()
>>> f = pysal.lib.io.open(pysal.lib.examples.get_path("sids2.dbf"))
>>> x = np.array(f.by_col['SIDR79'])
>>> y = np.array(f.by_col['SIDR74'])
>>> from pysal.explore.esda.moran import Moran_Local_BV
>>> lm =Moran_Local_BV(x, y, w, transformation = "r",                                permutations = 99)
>>> lm.q[:10]
array([3, 4, 3, 4, 2, 1, 4, 4, 2, 4])
>>> lm = Moran_Local_BV(x, y, w, transformation = "r",                                permutations = 99, geoda_quads=True)
>>> lm.q[:10]
array([2, 4, 2, 4, 3, 1, 4, 4, 3, 4])

Note random components result is slightly different values across architectures so the results have been removed from doctests and will be moved into unittests that are conditional on architectures

Attributes:
zx : array

original x variable standardized by mean and std

zy : array

original y variable standardized by mean and std

w : W

original w object

permutations : int

number of random permutations for calculation of pseudo p_values

Is : float

value of Moran’s I

q : array

(if permutations>0) values indicate quandrant location 1 HH, 2 LH, 3 LL, 4 HL

sim : array

(if permutations>0) vector of I values for permuted samples

p_sim : array

(if permutations>0) p-value based on permutations (one-sided) null: spatial randomness alternative: the observed Ii is further away or extreme from the median of simulated values. It is either extremelyi high or extremely low in the distribution of simulated Is.

EI_sim : array

(if permutations>0) average values of local Is from permutations

VI_sim : array

(if permutations>0) variance of Is from permutations

seI_sim : array

(if permutations>0) standard deviations of Is under permutations.

z_sim : arrray

(if permutations>0) standardized Is based on permutations

p_z_sim : array

(if permutations>0) p-values based on standard normal approximation from permutations (one-sided) for two-sided tests, these values should be multiplied by 2

Methods

by_col(df, x[, y, w, inplace, pvalue, outvals]) Function to compute a Moran_Local_BV statistic on a dataframe
calc  
__init__(x, y, w, transformation='r', permutations=999, geoda_quads=False)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(x, y, w[, transformation, …]) Initialize self.
by_col(df, x[, y, w, inplace, pvalue, outvals]) Function to compute a Moran_Local_BV statistic on a dataframe
calc(w, zx, zy)