def zscore(self, baseline=0.0): """ Return a parametric approximation of the z-score associated with the null hypothesis: (H0) 'contrast equals baseline' """ if self._pvalue == None or not self._baseline == baseline: self._pvalue = self.pvalue(baseline) # Avoid inf values kindly supplied by scipy. from nipy.neurospin.utils.zscore import zscore z = zscore(self._pvalue) return z
def zscore(self, baseline=0.0): """ Return a parametric approximation of the z-score associated with the null hypothesis: (H0) 'contrast equals baseline' """ if self._pvalue == None or not self._baseline == baseline: self._pvalue = self.pvalue(baseline) # Avoid inf values kindly supplied by scipy. """ z = sps.norm.isf(self._pvalue) th = z[np.where(z<np.inf)].max() z = np.minimum(z, int(th+1)) """ from nipy.neurospin.utils.zscore import zscore z = zscore(self._pvalue) return z