print("MCI partial correlations")
print(results['val_matrix'].round(2))
# In[10]:
q_matrix = pcmci.get_corrected_pvalues(p_matrix=results['p_matrix'],
fdr_method='fdr_bh')
pcmci.print_significant_links(p_matrix=results['p_matrix'],
q_matrix=q_matrix,
val_matrix=results['val_matrix'],
alpha_level=0.01)
# In[11]:
link_matrix = pcmci.return_significant_links(pq_matrix=q_matrix,
val_matrix=results['val_matrix'],
alpha_level=0.01)['link_matrix']
# In[12]:
link_matrix.shape
# In[13]:
tp.plot_graph(
figsize=(5, 5),
val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=var_names,
link_colorbar_label='cross-MCI',
node_colorbar_label='auto-MCI',
dataframe=dataframe,
cond_ind_test=parcorr,
verbosity=1)
#
min_lag, max_lag = 1,6
results = pcmci.run_pcmci(tau_min = min_lag, tau_max=max_lag, pc_alpha=None)
#
q_matrix = pcmci.get_corrected_pvalues(p_matrix=results['p_matrix'], fdr_method='fdr_bh')
#
pcmci.print_significant_links(
p_matrix = results['p_matrix'],
q_matrix = q_matrix,
val_matrix = results['val_matrix'],
alpha_level = 0.05)
link_matrix = pcmci.return_significant_links(pq_matrix = results['p_matrix'],
val_matrix=results['val_matrix'], alpha_level=0.05)['link_matrix']
tp.plot_graph(
val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=study_data.columns,
link_colorbar_label='cross-MCI',
node_colorbar_label='auto-MCI',
)
plt.show()
#dataframe = dataframe.iloc[:,:-4]
G = nx.DiGraph()
for i , node_name in enumerate(dataframe.var_names):
# G.add_node((i,{'name':node_name}))
G.add_node(i,name = node_name, influenced_by = 0)
