def correlation_p_pearsonr2(data0, data1):
r = stats.pearsonr(data0, data1)
if math.isnan(r):
return float('NaN')
if r == 1.:
return 0.
n = len(data0)
assert n == len(data1)
# Compute observed t statistic.
t = r * math.sqrt((n - 2) / (1 - r**2))
# Compute p-value for two-sided t-test.
return 2 * stats.t_cdf(-abs(t), n - 2)
def correlation_p_pearsonr2(data0, data1):
r = stats.pearsonr(data0, data1)
if math.isnan(r):
return float('NaN')
if r == 1.:
return 0.
n = len(data0)
assert n == len(data1)
# Compute observed t statistic.
t = r * math.sqrt((n - 2)/(1 - r**2))
# Compute p-value for two-sided t-test.
return 2 * stats.t_cdf(-abs(t), n - 2)
def correlation_pearsonr2(data0, data1):
r = stats.pearsonr(data0, data1)
return r**2
def correlation_pearsonr2(data0, data1):
r = stats.pearsonr(data0, data1)
return r**2
def test_pearsonr():
assert math.isnan(stats.pearsonr([], []))
assert stats.pearsonr([1,2,3], [2,4,6]) == +1.0
assert stats.pearsonr([1,2,3], [-2,-4,-6]) == -1.0
assert stats.pearsonr([1,2,3], [6,4,2]) == -1.0
assert stats.pearsonr([1,2,3], [+1,-1,+1]) == 0.0
def correlation_pearsonr2(data0, data1):
return stats.pearsonr(data0, data1)**2
