def tidy(fit):
from statsmodels.iolib.summary import summary_params_frame
tidied = summary_params_frame(fit).reset_index()
rename_cols = {
'index': 'term',
'coef': 'estimate',
'std err': 'std_err',
't': 'statistic',
'P>|t|': 'p_value',
'Conf. Int. Low': 'conf_int_low',
'Conf. Int. Upp.': 'conf_int_high'
}
return tidied.rename(columns=rename_cols)
def summary_frame(self, xname=None, alpha=0.05):
if self.effect is not None:
# we have everything for a params table
use_t = (self.distribution == 't')
yname='constraints' # Not used in params_frame
if xname is None:
xname = ['c%d'%ii for ii in range(len(self.effect))]
from statsmodels.iolib.summary import summary_params_frame
summ = summary_params_frame((self, self.effect, self.sd,
self.statistic,self.pvalue,
self.conf_int(alpha)), yname=yname,
xname=xname, use_t=use_t)
return summ
else:
# TODO: create something nicer
raise NotImplementedError('only available for t and z')
def summary_frame(self, xname=None, alpha=0.05):
"""Return the parameter table as a pandas DataFrame
This is only available for t and normal tests
"""
if self.effect is not None:
# we have everything for a params table
use_t = (self.distribution == 't')
yname = 'constraints' # Not used in params_frame
if xname is None:
xname = self.c_names
from statsmodels.iolib.summary import summary_params_frame
summ = summary_params_frame(
(self, self.effect, self.sd, self.statistic, self.pvalue,
self.conf_int(alpha)),
yname=yname,
xname=xname,
use_t=use_t,
alpha=alpha)
return summ
else:
# TODO: create something nicer
raise NotImplementedError('only available for t and z')
