esda.Join_Counts¶
- class esda.Join_Counts(y, w, permutations=999, drop_islands=True)[source]¶
Binary Join Counts
- Parameters:
- w
W
|Graph
spatial weights instance as W or Graph aligned with y
Notes
Technical details and derivations can be found in [CO81].
Examples
>>> import numpy as np
>>> import libpysal
>>> w = libpysal.weights.lat2W(4, 4)
>>> y = np.ones(16)
>>> y[0:8] = 0
>>> np.random.seed(12345)
>>> from 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
>>> round(jc.chi2_p, 3)
0.004
>>> jc.p_sim_chi2
0.002
- Attributes:
- w
W
original w object
vector of bb values for permuted samples
- (if permutations>0)
p-value based on permutations (one-sided) null: spatial randomness alternative: the observed bb is greater than under randomness
DataFrame
Contingency table for observed join counts
DataFrame
Expected contingency table for the null
libpysal.weights.to_adjlist()
.
Methods
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Function to compute a Join_Count statistic on a dataframe |
- classmethod by_col(df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws)[source]¶
Function to compute a Join_Count statistic on a dataframe
- Parameters:
- w
W
|Graph
spatial weights instance as W or Graph aligned with the dataframe. If not provided, this is searched for in the dataframe’s metadata
return a series contaning the results of the computation. If
operating inplace, the derived columns will be named
‘column_join_count’
the Join_Count statistic’s documentation for available p-values
Join_Count statistic
documentation for the Join_Count statistic.
dataframe
with
the
relevant
columns
attached.