针对pulse-transit的工具

dist/client/mne/stats/multi_comp.py

@@ -0,0 +1,96 @@
+# Authors: The MNE-Python contributors.
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+import numpy as np
+
+
+def _ecdf(x):
+    """No frills empirical cdf used in fdrcorrection."""
+    nobs = len(x)
+    return np.arange(1, nobs + 1) / float(nobs)
+
+
+def fdr_correction(pvals, alpha=0.05, method="indep"):
+    """P-value correction with False Discovery Rate (FDR).
+
+    Correction for multiple comparison using FDR :footcite:`GenoveseEtAl2002`.
+
+    This covers Benjamini/Hochberg for independent or positively correlated and
+    Benjamini/Yekutieli for general or negatively correlated tests.
+
+    Parameters
+    ----------
+    pvals : array_like
+        Set of p-values of the individual tests.
+    alpha : float
+        Error rate.
+    method : 'indep' | 'negcorr'
+        If 'indep' it implements Benjamini/Hochberg for independent or if
+        'negcorr' it corresponds to Benjamini/Yekutieli.
+
+    Returns
+    -------
+    reject : array, bool
+        True if a hypothesis is rejected, False if not.
+    pval_corrected : array
+        P-values adjusted for multiple hypothesis testing to limit FDR.
+
+    References
+    ----------
+    .. footbibliography::
+    """
+    pvals = np.asarray(pvals)
+    shape_init = pvals.shape
+    pvals = pvals.ravel()
+
+    pvals_sortind = np.argsort(pvals)
+    pvals_sorted = pvals[pvals_sortind]
+    sortrevind = pvals_sortind.argsort()
+
+    if method in ["i", "indep", "p", "poscorr"]:
+        ecdffactor = _ecdf(pvals_sorted)
+    elif method in ["n", "negcorr"]:
+        cm = np.sum(1.0 / np.arange(1, len(pvals_sorted) + 1))
+        ecdffactor = _ecdf(pvals_sorted) / cm
+    else:
+        raise ValueError("Method should be 'indep' and 'negcorr'")
+
+    reject = pvals_sorted < (ecdffactor * alpha)
+    if reject.any():
+        rejectmax = max(np.nonzero(reject)[0])
+    else:
+        rejectmax = 0
+    reject[:rejectmax] = True
+
+    pvals_corrected_raw = pvals_sorted / ecdffactor
+    pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]
+    pvals_corrected[pvals_corrected > 1.0] = 1.0
+    pvals_corrected = pvals_corrected[sortrevind].reshape(shape_init)
+    reject = reject[sortrevind].reshape(shape_init)
+    return reject, pvals_corrected
+
+
+def bonferroni_correction(pval, alpha=0.05):
+    """P-value correction with Bonferroni method.
+
+    Parameters
+    ----------
+    pval : array_like
+        Set of p-values of the individual tests.
+    alpha : float
+        Error rate.
+
+    Returns
+    -------
+    reject : array, bool
+        True if a hypothesis is rejected, False if not.
+    pval_corrected : array
+        P-values adjusted for multiple hypothesis testing to limit FDR.
+    """
+    pval = np.asarray(pval)
+    pval_corrected = pval * float(pval.size)
+    # p-values must not be larger than 1.
+    pval_corrected = pval_corrected.clip(max=1.0)
+    reject = pval_corrected < alpha
+    return reject, pval_corrected