im = ax.imshow(b_vols[:, :, 45, i], cmap="gray")
fig.subplots_adjust(right=0.85)
cax = fig.add_axes([0.9, 0.15, 0.03, 0.7])
fig.colorbar(im, cax=cax)
plt.savefig("../../../data/maps/dct_beta.png")
plt.close()
# To test significance of betas:
# Create contrast matrix for each beta:
c_mat = np.diag(np.array(np.ones((dct_design_mat.shape[1],))))
# t statistics and p values
# Length is the number of voxels after masking
t_mat = np.ones((dct_design_mat.shape[1], y.shape[1]))
p_mat = np.ones((dct_design_mat.shape[1], y.shape[1]))
for i in range(0, dct_design_mat.shape[1], 1):
t, p = linear_modeling.t_stat(X, c_mat[:, i], beta, MRSS, df)
t_mat[i, :] = t
p_mat[i, :] = p
# Reshape t values
t_val = np.zeros(vol_shape + (t_mat.shape[0],))
t_val[in_brain_mask, :] = t_mat.T
# Reshape p values
p_val = np.zeros(vol_shape + (p_mat.shape[0],))
p_val[in_brain_mask, :] = p_mat.T
# Visualizing t values for the middle slice
fig, axes = plt.subplots(nrows=2, ncols=3)
for i, ax in zip(range(0, 7, 1), axes.flat):
im = ax.imshow(t_val[:, :, 45, i], cmap="RdYlBu")
fig.subplots_adjust(right=0.85)
smooth_data = linear_modeling.smoothing(data, mask)
# Block linear regression
betas_hat, s2, df = linear_modeling.beta_est(smooth_data, design) #(4, 194287)
np.savetxt('../../../data/beta/' + f2 + '_betas_hat_block.txt', betas_hat.T, newline='\r\n')
# Filling back to raw data shape
beta_vols = np.zeros(vol_shape + (betas_hat.shape[0],)) #(91, 109, 91, 4)
beta_vols[mask] = betas_hat.T
# set regions outside mask as missing with np.nan
mean_data[~mask] = np.nan
beta_vols[~mask] = np.nan
# T-test on null hypothesis, assume only input variance of beta3 [0,0,0,1]
t_value, p_value = linear_modeling.t_stat(design, [0,1,1,1], betas_hat, s2, df) #(1, 194287) (1, 194287)
# Filling T, P value back to raw data shape (91, 109, 91)
t_vols = np.zeros(vol_shape + (t_value.shape[0],))
t_vols[mask, :] = t_value.T
p_vols = np.ones(vol_shape + (p_value.shape[0],))
p_vols[mask, :] = p_value.T
#========================================================================================================
# P value for single voxel
plt.figure(0)
plt.plot(range(p_value.shape[1]), p_value[0,:])
plt.xlabel('voxel')
plt.ylabel('P-value')
plt.title('P-value for single voxel')
line = plt.axhline(0.01, ls='--', color = 'red')
fig.colorbar(im, cax=cax)
plt.savefig("../../../data/maps/dct_beta.png")
plt.close()
# To test significance of betas:
# Create contrast matrix for each beta:
c_mat = np.diag(np.array(np.ones((dct_design_mat.shape[1], ))))
# t statistics and p values
# Length is the number of voxels after masking
t_mat = np.ones((dct_design_mat.shape[1], y.shape[1]))
p_mat = np.ones((
dct_design_mat.shape[1],
y.shape[1],
))
for i in range(0, dct_design_mat.shape[1], 1):
t, p = linear_modeling.t_stat(X, c_mat[:, i], beta, MRSS, df)
t_mat[i, :] = t
p_mat[i, :] = p
# Reshape t values
t_val = np.zeros(vol_shape + (t_mat.shape[0], ))
t_val[in_brain_mask, :] = t_mat.T
# Reshape p values
p_val = np.zeros(vol_shape + (p_mat.shape[0], ))
p_val[in_brain_mask, :] = p_mat.T
# Visualizing t values for the middle slice
fig, axes = plt.subplots(nrows=2, ncols=3)
for i, ax in zip(range(0, 7, 1), axes.flat):
im = ax.imshow(t_val[:, :, 45, i], cmap='RdYlBu')
fig.subplots_adjust(right=0.85)