for model_name, model, test, hypoth in zip(*model_configs):
for bold_name in asbold:
l = df.shape[0]
noi, prng = white(l, prng=prng)
df['bold'] = create_bold([df[bold_name].values], None, noi)
smo = smf.ols(model, data=df).fit()
print(smo.summary2())
stato = None
if test == 't':
stato = smo.t_test(hypoth)
elif test == 'F':
stato = smo.f_test(hypoth)
elif test is not None:
raise ValueError("Unknown test")
savedf = None
if args.save_behave:
savedf = df
n_results.update(merge_results(
'bold:'+bold_name + '_' + 'model:'+model_name,
model, smo, df=savedf, stato=stato, other=rlpars
))
results.update({str(n) : n_results})
write_hdf(results, str(args.name))
# Hypoth
stato = None
if test == 't':
stato = smo.t_test(hypoth)
elif test == 'F':
stato = smo.f_test(hypoth)
elif test is not None:
raise ValueError("Unknown test")
# Reformat and update store
savedf = None
if args.save_behave:
savedf = df_pred
n_results.update(
merge_results('_'.join([
bold_name,
str(alpha_bold), model_name,
str(alpha_pred)
]),
model,
smo,
df=savedf,
stato=stato,
other=None))
# Upate iter store
results.update({str(n): n_results})
# Write results at the end (only)
write_hdf(results, str(args.name))
# Hypoth
stato = None
if test == 't':
stato = smo.t_test(hypoth)
elif test == 'F':
stato = smo.f_test(hypoth)
elif test is not None:
raise ValueError("Unknown test")
# Reformat and update store
savedf = None
if args.save_behave:
savedf = df_pred
n_results.update(merge_results(
'_'.join([
bold_name, str(alpha_bold),
model_name, str(alpha_pred)]
),
model,
smo,
df=savedf,
stato=stato,
other=None
))
# Upate iter store
results.update({str(n) : n_results})
# Write results at the end (only)
write_hdf(results, str(args.name))
# Make bold
df['bold'] = create_bold([df[bold_name].values], None, noi)
# Regress
smo = smf.ols(model, data=df).fit()
print(smo.summary2())
# Hypoth test
stato = None
if test == 't':
stato = smo.t_test(hypoth)
elif test == 'F':
stato = smo.f_test(hypoth)
elif test is not None:
raise ValueError("Unknown test")
# Reformat
savedf = None
if args.save_behave:
savedf = df
n_results.update(merge_results(
bold_name + '_' + model_name,
model, smo, df=savedf, stato=stato, other=rlpars
))
results.update({str(n) : n_results})
write_hdf(results, str(args.name))
