def stat(self, baseline=0.0):
""" Return the decision statistic associated with the test of the
null hypothesis: (H0) 'contrast equals baseline'
Parameters
==========
baseline: float, optional,
Baseline value for the test statistic
"""
self.baseline = baseline
# Case: one-dimensional contrast ==> t or t**2
if self.dim == 1:
# avoids division by zero
stat = (self.effect - baseline) / np.sqrt(
np.maximum(self.variance, self.tiny))
if self.contrast_type == 'F':
stat = stat**2
# Case: F contrast
elif self.contrast_type == 'F':
# F = |t|^2/q , |t|^2 = e^t inv(v) e
if self.effect.ndim == 1:
self.effect = self.effect[np.newaxis]
if self.variance.ndim == 1:
self.variance = self.variance[np.newaxis, np.newaxis]
stat = (
multiple_mahalanobis(self.effect - baseline, self.variance) /
self.dim)
# Case: tmin (conjunctions)
elif self.contrast_type == 'tmin-conjunction':
vdiag = self.variance.reshape(
[self.dim**2] + list(self.variance.shape[2:]))[::self.dim + 1]
stat = (self.effect - baseline) / np.sqrt(
np.maximum(vdiag, self.tiny))
stat = stat.min(0)
# Unknwon stat
else:
raise ValueError('Unknown statistic type')
self.stat_ = stat
return stat.ravel()
def stat(self, baseline=0.0):
""" Return the decision statistic associated with the test of the
null hypothesis: (H0) 'contrast equals baseline'
Parameters
==========
baseline: float, optional,
Baseline value for the test statistic
"""
self.baseline = baseline
# Case: one-dimensional contrast ==> t or t**2
if self.dim == 1:
# avoids division by zero
stat = (self.effect - baseline) / np.sqrt(
np.maximum(self.variance, self.tiny))
if self.contrast_type == 'F':
stat = stat ** 2
# Case: F contrast
elif self.contrast_type == 'F':
# F = |t|^2/q , |t|^2 = e^t inv(v) e
if self.effect.ndim == 1:
self.effect = self.effect[np.newaxis]
if self.variance.ndim == 1:
self.variance = self.variance[np.newaxis, np.newaxis]
stat = (multiple_mahalanobis(self.effect - baseline,
self.variance) / self.dim)
# Case: tmin (conjunctions)
elif self.contrast_type == 'tmin-conjunction':
vdiag = self.variance.reshape([self.dim ** 2] + list(
self.variance.shape[2:]))[:: self.dim + 1]
stat = (self.effect - baseline) / np.sqrt(
np.maximum(vdiag, self.tiny))
stat = stat.min(0)
# Unknwon stat
else:
raise ValueError('Unknown statistic type')
self.stat_ = stat
return stat.ravel()