Hi,
Suppose I wanted to test the independence of two boolean variables using
Chi-Square:
>>> X = numpy.vstack(([[0,0]] * 18, [[0,1]] * 7, [[1,0]] * 42, [[1,1]] *
33))
>>> X.shape
(100, 2)
I'd like to understand the difference between doing:
>>> sklearn.feature_selection.chi2(X[:,[0]], X[:,1])
(array([ 0.5]), array([ 0.47950012]))
and doing:
>>> pandas.crosstab(X[:,0], X[:,1])
col_0 0 1
row_0
0 18 7
1 42 33
>>> scipy.stats.chi2_contingency(pd.crosstab(X[:,0], X[:,1]),
correction=False)
(2.0, 0.15729920705028505, 1, array([[ 15., 10.],
[ 45., 30.]]))
What explains the difference in terms of the Chi-Square value (0.5 vs 2)
and the P-value (0.48 vs 0.157)?
Thanks,
Christian
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