# set up model and fit linear model
linear_model = LinearRegression(fit_intercept=False)
linear_model.fit(design, Y)
# extract the coefficients for linear model estimator
betas = get_coef(linear_model, 'coef_')
# calculate coefficient of determination (r-squared)
r_squared = r2_score(Y, linear_model.predict(design), multioutput='raw_values')
# project r-squared back to channels by times space
r_squared = r_squared.reshape((n_channels, n_times))
r_squared = EvokedArray(r_squared, epochs_info, tmin)
###############################################################################
# plot model r-squared
# only show -250 to 500 ms
ts_args = dict(xlim=(-.25, 0.5), unit=False, ylim=dict(eeg=[0, 0.8]))
topomap_args = dict(cmap='Reds',
scalings=dict(eeg=1),
vmin=0,
vmax=0.8,
average=0.05)
# create plot
fig = r_squared.plot_joint(ts_args=ts_args,
topomap_args=topomap_args,
title='Proportion of variance explained by '
'predictors',
times=[.13, .23])
fig.axes[0].set_ylabel('R-squared')
# arguments fot the topographical maps
topomap_args = dict(cmap='magma_r',
scalings=dict(eeg=1),
sensors=False,
time_unit='ms',
vmin=0.0,
vmax=F.max(),
average=0.05,
extrapolate='head',
outlines='head')
title = 'Main effect incong. vs cong.'
fig = F_evoked.plot_joint(ttp,
ts_args=ts_args,
topomap_args=topomap_args,
title=title,
show=False)
fig.axes[-1].texts[0]._fontproperties._size = 12.0 # noqa
fig.axes[-1].texts[0]._fontproperties._weight = 'bold' # noqa
fig.axes[0].set_xticks(list(np.arange(-0.25, 1.05, 0.25)), minor=False)
fig.axes[0].set_xticklabels(list(np.arange(-250, 1005, 250)))
fig.axes[0].set_xlabel('Time (ms)')
fig.axes[0].set_yticks(list(np.arange(0.0, F.max() + 2, 4)), minor=False)
fig.axes[0].axvline(x=0.0,
ymin=0.0,
ymax=F.max() + 2,
color='black',
linestyle='dashed',
linewidth=.8)
fig.axes[0].spines['top'].set_visible(False)
