netneurotools.stats.permtest_pearsonr
- netneurotools.stats.permtest_pearsonr(a, b, axis=0, n_perm=1000, resamples=None, seed=0)[source]
Non-parametric equivalent of
scipy.stats.pearsonr().Generates two-tailed p-value for hypothesis of whether samples a and b are correlated using permutation tests
- Parameters:
a,b ((N[, M]) array_like) – Sample observations. These arrays must have the same length and either an equivalent number of columns or be broadcastable
axis (int or None, optional) – Axis along which to compute test. If None, compute over whole arrays of a and b. Default: 0
n_perm (int, optional) – Number of permutations to assess. Unless a and b are very small along axis this will approximate a randomization test via Monte Carlo simulations. Default: 1000
resamples ((N, P) array_like, optional) – Resampling array used to shuffle a when generating null distribution of correlations. This array must have the same length as a and b and should have at least the same number of columns as n_perm (if it has more then only n_perm columns will be used. When not specified a standard permutation is used to shuffle a. Default: None
seed ({int, np.random.RandomState instance, None}, optional) – Seed for random number generation. Set to None for “randomness”. Default: 0
- Returns:
corr (float or numpyndarray) – Correlations
pvalue (float or numpy.ndarray) – Non-parametric p-value
Notes
The lowest p-value that can be returned by this function is equal to 1 / (n_perm + 1).
Examples
>>> from netneurotools import stats
>>> np.random.seed(12345678) # set random seed for reproducible results >>> x, y = stats.make_correlated_xy(corr=0.1, size=100) >>> stats.permtest_pearsonr(x, y) (0.10032564626876286, 0.3046953046953047)
>>> x, y = stats.make_correlated_xy(corr=0.5, size=100) >>> stats.permtest_pearsonr(x, y) (0.500040365781984, 0.000999000999000999)
Also works with multiple columns by either broadcasting the smaller array to the larger:
>>> z = x + np.random.normal(loc=1, size=100) >>> stats.permtest_pearsonr(x, np.column_stack([y, z])) (array([0.50004037, 0.25843187]), array([0.000999 , 0.01098901]))
or by using matching columns in the two arrays (e.g., x and y vs a and b):
>>> a, b = stats.make_correlated_xy(corr=0.9, size=100) >>> stats.permtest_pearsonr(np.column_stack([x, a]), np.column_stack([y, b])) (array([0.50004037, 0.89927523]), array([0.000999, 0.000999]))
Examples using netneurotools.stats.permtest_pearsonr
Non-parametric significance testing with permutations
