def summary(self, yname=None, xname=None, title=0, alpha=.05,
return_fmt='text'):
"""
This is for testing the new summary setup
"""
from scikits.statsmodels.iolib.summary import (summary_top,
summary_params, summary_return)
## left = [(i, None) for i in (
## 'Dependent Variable:',
## 'Model type:',
## 'Method:',
## 'Date:',
## 'Time:',
## 'Number of Obs:',
## 'df resid',
## 'df model',
## )]
top_left = [('Dep. Variable:', None),
('Model:', None),
('Method:', ['IRLS']),
('Norm:', [self.fit_options['norm']]),
('Scale Est.:', [self.fit_options['scale_est']]),
('Cov Type:', [self.fit_options['cov']]),
('Date:', None),
('Time:', None),
('No. Iterations:', ["%d" % self.model.iteration]), #stale state?
]
top_right = [('No. Observations:', None),
('Df Residuals:', None),
('Df Model:', None)
]
if not title is None:
title = "Robust linear Model Regression Results"
#boiler plate
from scikits.statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[],
yname=yname, xname=xname, title=title)
smry.add_table_params(self, yname=yname, xname=xname, alpha=.05,
use_t=False)
#diagnostic table is not used yet
# smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
# yname=yname, xname=xname,
# title="")
#add warnings/notes, added to text format only
etext =[]
wstr = \
'''If the model instance has been used for another fit with different fit
parameters, then the fit options might not be the correct ones anymore .'''
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
def summary(self, yname=None, xname=None, title=None, alpha=.05):
"""Summarize the Regression Results
Parameters
-----------
yname : string, optional
Default is `y`
xname : list of strings, optional
Default is `var_##` for ## in p the number of regressors
title : string, optional
Title for the top table. If not None, then this replaces the
default title
alpha : float
significance level for the confidence intervals
Returns
-------
smry : Summary instance
this holds the summary tables and text, which can be printed or
converted to various output formats.
See Also
--------
scikits.statsmodels.iolib.summary.Summary : class to hold summary
results
"""
top_left = [('Dep. Variable:', None),
('Model:', [self.model.__class__.__name__]),
('Method:', ['MLE']),
('Date:', None),
('Time:', None),
('No. iterations:', ["%d" % self.mle_retvals['iterations']]),
('converged:', ["%s" % self.mle_retvals['converged']])
]
top_right = [('No. Observations:', None),
('Df Residuals:', None),
('Df Model:', None),
('Pseudo R-squ.:', ["%#6.4g" % self.prsquared]),
('Log-Likelihood:', None),
('LL-Null:', ["%#8.5g" % self.llnull]),
('LLR p-value:', ["%#6.4g" % self.llr_pvalue])
]
if title is None:
title = self.model.__class__.__name__ + ' ' + "Regression Results"
#boiler plate
from scikits.statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[],
yname=yname, xname=xname, title=title)
smry.add_table_params(self, yname=yname, xname=xname, alpha=.05,
use_t=True)
#diagnostic table not used yet
# smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
# yname=yname, xname=xname,
# title="")
#TODO: attach only to binary models
if self.model.__class__.__name__ in ['Logit', 'Probit']:
fittedvalues = self.model.cdf(self.fittedvalues)
absprederror = np.abs(self.model.endog - fittedvalues)
predclose_sum = (absprederror < 1e-4).sum()
predclose_frac = predclose_sum / len(fittedvalues)
#add warnings/notes
etext =[]
if predclose_sum == len(fittedvalues): #nobs?
wstr = \
'''Complete Separation: The results show that there is complete separation.
In this case the Maximum Likelihood Estimator does not exist and the parameters
are not identified.'''
etext.append(wstr)
elif predclose_frac > 0.1: #TODO: get better diagnosis
wstr = \
'''Possibly complete quasi-separation: A fraction %f4.2 of observations can be
perfectly predicted. This might indicate that there is complete
quasi-separation. In this case some parameters will not be identified.''' % predclose_frac
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
def summary(self, yname=None, xname=None, title=None, alpha=.05):
"""Summarize the Regression Results
Parameters
-----------
yname : string, optional
Default is `y`
xname : list of strings, optional
Default is `var_##` for ## in p the number of regressors
title : string, optional
Title for the top table. If not None, then this replaces the
default title
alpha : float
significance level for the confidence intervals
Returns
-------
smry : Summary instance
this holds the summary tables and text, which can be printed or
converted to various output formats.
See Also
--------
scikits.statsmodels.iolib.summary.Summary : class to hold summary
results
"""
top_left = [
('Dep. Variable:', None),
('Model:', None),
('Model Family:', [self.family.__class__.__name__]),
('Link Function:', [self.family.link.__class__.__name__]),
('Method:', ['IRLS']),
('Date:', None),
('Time:', None),
('No. Iterations:', ["%d" % self.model.iteration]),
]
top_right = [('No. Observations:', None), ('Df Residuals:', None),
('Df Model:', None), ('Scale:', [self.scale]),
('Log-Likelihood:', None),
('Deviance:', ["%#8.5g" % self.deviance]),
('Pearson chi2:', ["%#6.3g" % self.pearson_chi2])]
if title is None:
title = "Generalized Linear Model Regression Results"
#create summary tables
from scikits.statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(
self,
gleft=top_left,
gright=top_right, #[],
yname=yname,
xname=xname,
title=title)
smry.add_table_params(self,
yname=yname,
xname=xname,
alpha=.05,
use_t=True)
#diagnostic table is not used yet:
# smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
# yname=yname, xname=xname,
# title="")
return smry
def summary(self,
yname=None,
xname=None,
title=0,
alpha=.05,
return_fmt='text'):
"""
This is for testing the new summary setup
"""
from scikits.statsmodels.iolib.summary import (summary_top,
summary_params,
summary_return)
## left = [(i, None) for i in (
## 'Dependent Variable:',
## 'Model type:',
## 'Method:',
## 'Date:',
## 'Time:',
## 'Number of Obs:',
## 'df resid',
## 'df model',
## )]
top_left = [
('Dep. Variable:', None),
('Model:', None),
('Method:', ['IRLS']),
('Norm:', [self.fit_options['norm']]),
('Scale Est.:', [self.fit_options['scale_est']]),
('Cov Type:', [self.fit_options['cov']]),
('Date:', None),
('Time:', None),
('No. Iterations:', ["%d" % self.model.iteration]), #stale state?
]
top_right = [('No. Observations:', None), ('Df Residuals:', None),
('Df Model:', None)]
if not title is None:
title = "Robust linear Model Regression Results"
#boiler plate
from scikits.statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(
self,
gleft=top_left,
gright=top_right, #[],
yname=yname,
xname=xname,
title=title)
smry.add_table_params(self,
yname=yname,
xname=xname,
alpha=.05,
use_t=False)
#diagnostic table is not used yet
# smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
# yname=yname, xname=xname,
# title="")
#add warnings/notes, added to text format only
etext = []
wstr = \
'''If the model instance has been used for another fit with different fit
parameters, then the fit options might not be the correct ones anymore .'''
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
def summary(self,
yname=None,
xname=None,
title=None,
alpha=.05,
yname_list=None):
"""Summarize the Regression Results
Parameters
-----------
yname : string, optional
Default is `y`
xname : list of strings, optional
Default is `var_##` for ## in p the number of regressors
title : string, optional
Title for the top table. If not None, then this replaces the
default title
alpha : float
significance level for the confidence intervals
Returns
-------
smry : Summary instance
this holds the summary tables and text, which can be printed or
converted to various output formats.
See Also
--------
scikits.statsmodels.iolib.summary.Summary : class to hold summary
results
"""
top_left = [
('Dep. Variable:', None),
('Model:', [self.model.__class__.__name__]),
('Method:', ['MLE']),
('Date:', None),
('Time:', None),
# ('No. iterations:', ["%d" % self.mle_retvals['iterations']]),
('converged:', ["%s" % self.mle_retvals['converged']])
]
top_right = [('No. Observations:', None), ('Df Residuals:', None),
('Df Model:', None),
('Pseudo R-squ.:', ["%#6.4g" % self.prsquared]),
('Log-Likelihood:', None),
('LL-Null:', ["%#8.5g" % self.llnull]),
('LLR p-value:', ["%#6.4g" % self.llr_pvalue])]
if title is None:
title = self.model.__class__.__name__ + ' ' + "Regression Results"
#boiler plate
from scikits.statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(
self,
gleft=top_left,
gright=top_right, #[],
yname=yname,
xname=xname,
title=title)
if yname_list is None:
yname_list = yname
smry.add_table_params(self,
yname=yname_list,
xname=xname,
alpha=.05,
use_t=True)
#diagnostic table not used yet
# smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
# yname=yname, xname=xname,
# title="")
#TODO: attach only to binary models
if self.model.__class__.__name__ in ['Logit', 'Probit']:
fittedvalues = self.model.cdf(self.fittedvalues)
absprederror = np.abs(self.model.endog - fittedvalues)
predclose_sum = (absprederror < 1e-4).sum()
predclose_frac = predclose_sum / len(fittedvalues)
#add warnings/notes
etext = []
if predclose_sum == len(fittedvalues): #nobs?
wstr = \
'''Complete Separation: The results show that there is complete separation.
In this case the Maximum Likelihood Estimator does not exist and the parameters
are not identified.'''
etext.append(wstr)
elif predclose_frac > 0.1: #TODO: get better diagnosis
wstr = \
'''Possibly complete quasi-separation: A fraction %f4.2 of observations can be
perfectly predicted. This might indicate that there is complete
quasi-separation. In this case some parameters will not be identified.''' % predclose_frac
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
def summary(self, yname=None, xname=None, title=None, alpha=.05):
"""Summarize the Regression Results
Parameters
-----------
yname : string, optional
Default is `y`
xname : list of strings, optional
Default is `var_##` for ## in p the number of regressors
title : string, optional
Title for the top table. If not None, then this replaces the
default title
alpha : float
significance level for the confidence intervals
Returns
-------
smry : Summary instance
this holds the summary tables and text, which can be printed or
converted to various output formats.
See Also
--------
scikits.statsmodels.iolib.summary.Summary : class to hold summary
results
"""
top_left = [('Dep. Variable:', None),
('Model:', None),
('Model Family:', [self.family.__class__.__name__]),
('Link Function:', [self.family.link.__class__.__name__]),
('Method:', ['IRLS']),
('Date:', None),
('Time:', None),
('No. Iterations:', ["%d" % self.model.iteration]),
]
top_right = [('No. Observations:', None),
('Df Residuals:', None),
('Df Model:', None),
('Scale:', [self.scale]),
('Log-Likelihood:', None),
('Deviance:', ["%#8.5g" % self.deviance]),
('Pearson chi2:', ["%#6.3g" % self.pearson_chi2])
]
if title is None:
title = "Generalized Linear Model Regression Results"
#create summary tables
from scikits.statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[],
yname=yname, xname=xname, title=title)
smry.add_table_params(self, yname=yname, xname=xname, alpha=.05,
use_t=True)
#diagnostic table is not used yet:
# smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
# yname=yname, xname=xname,
# title="")
return smry