pysal.explore.esda.Join_Counts¶

class pysal.explore.esda.Join_Counts(y, w, permutations=999)[source]¶

Binary Join Counts

Parameters:	y : array binary variable measured across n spatial units w : W spatial weights instance permutations : int number of random permutations for calculation of pseudo-p_values

Notes

Technical details and derivations can be found in [CO81].

Examples

Replicate example from anselin and rey

>>> import numpy as np
>>> import pysal.lib
>>> w = pysal.lib.weights.lat2W(4, 4)
>>> y = np.ones(16)
>>> y[0:8] = 0
>>> np.random.seed(12345)
>>> from pysal.explore.esda.join_counts import Join_Counts
>>> jc = Join_Counts(y, w)
>>> jc.bb
10.0
>>> jc.bw
4.0
>>> jc.ww
10.0
>>> jc.J
24.0
>>> len(jc.sim_bb)
999
>>> round(jc.p_sim_bb, 3)
0.003
>>> round(np.mean(jc.sim_bb), 3)
5.547
>>> np.max(jc.sim_bb)
10.0
>>> np.min(jc.sim_bb)
0.0
>>> len(jc.sim_bw)
999
>>> jc.p_sim_bw
1.0
>>> np.mean(jc.sim_bw)
12.811811811811811
>>> np.max(jc.sim_bw)
24.0
>>> np.min(jc.sim_bw)
7.0
>>>

Attributes:

y : array original variable w : W original w object permutations : int number of permutations bb : float number of black-black joins ww : float number of white-white joins bw : float number of black-white joins J : float number of joins sim_bb : array (if permutations>0) vector of bb values for permuted samples p_sim_bb : array (if permutations>0) p-value based on permutations (one-sided) null: spatial randomness alternative: the observed bb is greater than under randomness mean_bb : float average of permuted bb values min_bb : float minimum of permuted bb values max_bb : float maximum of permuted bb values sim_bw : array (if permutations>0) vector of bw values for permuted samples p_sim_bw : array (if permutations>0) p-value based on permutations (one-sided) null: spatial randomness alternative: the observed bw is greater than under randomness mean_bw : float average of permuted bw values min_bw : float minimum of permuted bw values max_bw : float maximum of permuted bw values

Methods

by_col(df, cols[, w, inplace, pvalue, outvals])

Function to compute a Join_Count statistic on a dataframe

__init__(y, w, permutations=999)[source]¶

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

Methods

__init__(y, w[, permutations])	Initialize self.
by_col(df, cols[, w, inplace, pvalue, outvals])	Function to compute a Join_Count statistic on a dataframe