def test_pearsonr_difference_significance(self): # go through a few examples (calculations done using http://www.quantpsy.org/corrtest/corrtest.htm) r_a = 0.3639 n_a = 91 r_b = 0.0205 n_b = 63 p = 2 * 0.01556 self.assertAlmostEqual( p, stats.pearsonr_difference_significance(r_a, n_a, r_b, n_b), places=3, ) r_a = 0.3 n_a = 200 r_b = 0.1 n_b = 100 p = 2 * 0.04585 self.assertAlmostEqual( p, stats.pearsonr_difference_significance(r_a, n_a, r_b, n_b), places=3, ) r_a = 0.7 n_a = 30 r_b = -0.3 n_b = 10 p = 2 * 0.00276 self.assertAlmostEqual( p, stats.pearsonr_difference_significance(r_a, n_a, r_b, n_b), places=3, ) r_a = -0.1 n_a = 20 r_b = 0.4 n_b = 4 p = 2 * 0.30529 self.assertAlmostEqual( p, stats.pearsonr_difference_significance(r_a, n_a, r_b, n_b), places=3, )
def test_pearsonr_difference_significance(self): # go through a few examples (calculations done using http://www.quantpsy.org/corrtest/corrtest.htm) r_a = 0.3639 n_a = 91 r_b = 0.0205 n_b = 63 p = 2*0.01556 self.assertAlmostEqual( p, stats.pearsonr_difference_significance(r_a, n_a, r_b, n_b), places=3, ) r_a = 0.3 n_a = 200 r_b = 0.1 n_b = 100 p = 2*0.04585 self.assertAlmostEqual( p, stats.pearsonr_difference_significance(r_a, n_a, r_b, n_b), places=3, ) r_a = 0.7 n_a = 30 r_b = -0.3 n_b = 10 p = 2*0.00276 self.assertAlmostEqual( p, stats.pearsonr_difference_significance(r_a, n_a, r_b, n_b), places=3, ) r_a = -0.1 n_a = 20 r_b = 0.4 n_b = 4 p = 2*0.30529 self.assertAlmostEqual( p, stats.pearsonr_difference_significance(r_a, n_a, r_b, n_b), places=3, )
axs[AXES[expt.id]].fill_between( t, lbs, ubs, color=EARLY_LATE_COLORS[label], alpha=ALPHA ) early_late_handles.append(handle) early_late_labels.append(label) if label == 'early': # store these for later so we can compare them to the lates early_correlations = correlations early_ns = ns elif label == 'late': # calculate significance between early and late correlations p_vals = [] for r_1, n_1, r_2, n_2 in zip(early_correlations, early_ns, correlations, ns): p_vals.append(stats.pearsonr_difference_significance(r_1, n_1, r_2, n_2)) ax = axs_twin[AXES[expt.id] - 2] ax.plot(t, p_vals, c='k', ls='-', lw=2) ax.axhline(0.05, c='k', ls='--') ax.set_ylim(0, 0.5) axs_twin[AXES[expt.id] - 2].legend(early_late_handles, early_late_labels, loc='best') axs[0].legend(wind_speed_handles, wind_speed_labels, loc='best') axs[0].set_title('Concentration/heading\npartial correlations') axs[1].set_ylim(0, 1) axs[1].legend(wind_speed_handles, wind_speed_labels, loc='best') axs[1].set_title('P-values')
alpha=ALPHA) early_late_handles.append(handle) early_late_labels.append(label) if label == 'early': # store these for later so we can compare them to the lates early_correlations = correlations early_ns = ns elif label == 'late': # calculate significance between early and late correlations p_vals = [] for r_1, n_1, r_2, n_2 in zip(early_correlations, early_ns, correlations, ns): p_vals.append( stats.pearsonr_difference_significance( r_1, n_1, r_2, n_2)) ax = axs_twin[AXES[expt.id] - 2] ax.plot(t, p_vals, c='k', ls='-', lw=2) ax.axhline(0.05, c='k', ls='--') ax.set_ylim(0, 0.5) axs_twin[AXES[expt.id] - 2].legend(early_late_handles, early_late_labels, loc='best') axs[0].legend(wind_speed_handles, wind_speed_labels, loc='best') axs[0].set_title('Concentration/heading\npartial correlations') axs[1].set_ylim(0, 1)