netneurotools.stats.efficient_pearsonr

netneurotools.stats.efficient_pearsonr(a, b, ddof=1, nan_policy='propagate')[source]

Compute correlation of matching columns in a and b.

Parameters:

  • a (array_like) – Sample observations. These arrays must have the same length and either an equivalent number of columns or be broadcastable

  • b (array_like) – Sample observations. These arrays must have the same length and either an equivalent number of columns or be broadcastable

  • ddof (int, optional) – Degrees of freedom correction in the calculation of the standard deviation. Default: 1

  • nan_policy (bool, optional) – Defines how to handle when input contains nan. ‘propagate’ returns nan, ‘raise’ throws an error, ‘omit’ performs the calculations ignoring nan values. Default: ‘propagate’

Returns:

  • corr (float or numpy.ndarray) – Pearson’s correlation coefficient between matching columns of inputs

  • pval (float or numpy.ndarray) – Two-tailed p-values

Notes

If either input contains nan and nan_policy is set to ‘omit’, both arrays will be masked to omit the nan entries.

Examples

>>> from netneurotools import datasets, stats

Generate some not-very-correlated and some highly-correlated data:

>>> np.random.seed(12345678)  # set random seed for reproducible results
>>> x1, y1 = datasets.make_correlated_xy(corr=0.1, size=100)
>>> x2, y2 = datasets.make_correlated_xy(corr=0.8, size=100)

Calculate both correlations simultaneously:

>>> stats.efficient_pearsonr(np.c_[x1, x2], np.c_[y1, y2])
(array([0.10032565, 0.79961189]), array([3.20636135e-01, 1.97429944e-23]))