def _col_info(result, more_info=None):
'''Stack model info in a column
'''
model_info = summary_model(result)
default_info_ = OrderedDict()
default_info_['Model:'] = lambda x: x.get('Model:')
default_info_['No. Observations:'] = lambda x: x.get('No. Observations:')
default_info_['R-squared:'] = lambda x: x.get('R-squared:')
default_info_['Adj. R-squared:'] = lambda x: x.get('Adj. R-squared:')
default_info_['Pseudo R-squared:'] = lambda x: x.get('Pseudo R-squared:')
default_info_['F-statistic:'] = lambda x: x.get('F-statistic:')
default_info_['Covariance Type:'] = lambda x: x.get('Covariance Type:')
default_info_['Eeffects:'] = lambda x: x.get('Effects:')
default_info_['Covariance Type:'] = lambda x: x.get('Covariance Type:')
default_info = default_info_.copy()
for k,v in default_info_.items():
if v(model_info):
default_info[k] = v(model_info)
else:
default_info.pop(k) # pop the item whose value is none.
if more_info is None:
more_info = default_info
else:
if not isinstance(more_info,list):
more_info = [more_info]
for i in more_info:
try:
default_info[i] = getattr(result,i)
except (AttributeError, KeyError, NotImplementedError) as e:
raise e
more_info = default_info
try:
out = pd.DataFrame(more_info, index=[result.model.endog_names]).T
except (AttributeError):
out = pd.DataFrame(more_info, index=result.model.dependent.vars).T
return out