def confidence_interval(self):
"""The size of the 1-alpha confidence interval"""
coh_var = np.zeros((self.input.data.shape[0],
self.input.data.shape[0],
self._L), 'd')
for i in range(self.input.data.shape[0]):
for j in range(i):
if i != j:
coh_var[i, j] = tsu.jackknifed_coh_variance(
self.spectra[i],
self.spectra[j],
self.eigs,
adaptive=self._adaptive
)
idx = triu_indices(self.input.data.shape[0], 1)
coh_var[idx[0], idx[1], ...] = coh_var[idx[1], idx[0], ...].conj()
coh_mat_xform = tsu.normalize_coherence(self.coherence,
2 * self.df - 2)
lb = coh_mat_xform + dist.t.ppf(self.alpha / 2,
self.df - 1) * np.sqrt(coh_var)
ub = coh_mat_xform + dist.t.ppf(1 - self.alpha / 2,
self.df - 1) * np.sqrt(coh_var)
# convert this measure with the normalizing function
tsu.normal_coherence_to_unit(lb, 2 * self.df - 2, lb)
tsu.normal_coherence_to_unit(ub, 2 * self.df - 2, ub)
return ub - lb
"""
if i != j:
coh_var[i, j] = utils.jackknifed_coh_variance(
tspectra[i], tspectra[j], eigs, adaptive=True,
)
"""
This measure is normalized, based on the number of tapers:
"""
coh_mat_xform = utils.normalize_coherence(coh_mat, 2 * K - 2)
"""
We calculate 95% confidence intervals based on the jack-knife variance
calculation:
"""
t025_limit = coh_mat_xform + dist.t.ppf(.025, K - 1) * np.sqrt(coh_var)
t975_limit = coh_mat_xform + dist.t.ppf(.975, K - 1) * np.sqrt(coh_var)
utils.normal_coherence_to_unit(t025_limit, 2 * K - 2, t025_limit)
utils.normal_coherence_to_unit(t975_limit, 2 * K - 2, t975_limit)
"""
if i != j:
coh_var[i, j] = utils.jackknifed_coh_variance(
tspectra[i],
tspectra[j],
eigs,
adaptive=True,
)
"""
This measure is normalized, based on the number of tapers:
"""
coh_mat_xform = utils.normalize_coherence(coh_mat, 2 * K - 2)
"""
We calculate 95% confidence intervals based on the jack-knife variance
calculation:
"""
t025_limit = coh_mat_xform + dist.t.ppf(.025, K - 1) * np.sqrt(coh_var)
t975_limit = coh_mat_xform + dist.t.ppf(.975, K - 1) * np.sqrt(coh_var)
utils.normal_coherence_to_unit(t025_limit, 2 * K - 2, t025_limit)
utils.normal_coherence_to_unit(t975_limit, 2 * K - 2, t975_limit)
if L < n_samples:
freqs = np.linspace(0, 1 / (2 * TR), L)
