def summary(self, yname=None, xname=None, title=0, alpha=.05,
return_fmt='text'):
"""
This is for testing the new summary setup
"""
from 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.fit_history['iteration']])
]
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 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=0, alpha=.05,
return_fmt='text'):
"""
This is for testing the new summary setup
"""
from 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.fit_history['iteration']])
]
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 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=0, alpha=0.05, return_fmt="text"):
"""
This is for testing the new summary setup
"""
from 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.fit_history["iteration"]]),
]
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 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=alpha, use_t=self.use_t)
# 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):
"""Summary table of first-stage estimation results"""
stubs_lookup = {
'rsquared': 'R-squared',
'partial.rsquared': 'Partial R-squared',
'shea.rsquared': 'Shea\'s R-squared',
'f.stat': 'F-statistic',
'f.pval': 'P-value (F-stat)'
}
smry = Summary()
diagnostics = self.diagnostics
vals = [[_str(v) for v in diagnostics[c]] for c in diagnostics]
stubs = [stubs_lookup[s] for s in list(diagnostics.columns)]
header = list(diagnostics.index)
params = []
for var in header:
res = self.individual[var]
v = c_[res.params.values, res.tstats.values]
params.append(v.ravel())
params = array(params)
if params.ndim == 1:
params = params[:, None]
params_fmt = [[_str(val) for val in row] for row in params.T]
for i in range(1, len(params_fmt), 2):
for j in range(len(params_fmt[i])):
params_fmt[i][j] = '({0})'.format(params_fmt[i][j])
params_stub = []
for var in res.params.index:
params_stub.extend([var, ''])
title = 'First Stage Estimation Results'
vals = table_concat((vals, params_fmt))
stubs = stub_concat((stubs, params_stub))
txt_fmt = default_txt_fmt.copy()
txt_fmt['data_aligns'] = 'r'
txt_fmt['header_align'] = 'r'
table = SimpleTable(vals,
headers=header,
title=title,
stubs=stubs,
txt_fmt=txt_fmt)
smry.tables.append(table)
extra_txt = [
'T-stats reported in parentheses',
'T-stats use same covariance type as original model'
]
smry.add_extra_txt(extra_txt)
return smry
def summary(self,
yname=None,
xname=None,
title=0,
alpha=.05,
return_fmt='text'):
"""
This is for testing the new summary setup
"""
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.fit_history['iteration']])
]
top_right = [('No. Observations:', None), ('Df Residuals:', None),
('Df Model:', None)]
if title is not None:
title = "Robust linear Model Regression Results"
# boiler plate
from 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=alpha,
use_t=self.use_t)
# add warnings/notes, added to text format only
etext = []
wstr = ("If the model instance has been used for another fit "
"with different fit\n"
"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) -> Summary:
"""Summary of test, containing statistic, p-value and critical values"""
if self.bandwidth == int(self.bandwidth):
bw = str(int(self.bandwidth))
else:
bw = f"{self.bandwidth:0.3f}"
table_data = [
("Test Statistic", f"{self.stat:0.3f}"),
("P-value", f"{self.pvalue:0.3f}"),
("Kernel", f"{self.kernel}"),
("Bandwidth", bw),
]
title = self.name
table = SimpleTable(
table_data,
stubs=None,
title=title,
colwidths=18,
datatypes=[0, 1],
data_aligns=("l", "r"),
)
smry = Summary()
smry.tables.append(table)
cv_string = "Critical Values: "
for val in self.critical_values.keys():
p = str(int(val)) + "%"
cv_string += f"{self.critical_values[val]:0.2f}"
cv_string += " (" + p + ")"
cv_string += ", "
# Remove trailing ,<space>
cv_string = cv_string[:-2]
extra_text = [
"Trend: " + TREND_DESCRIPTION[self._trend],
cv_string,
"Null Hypothesis: " + self.null_hypothesis,
"Alternative Hypothesis: " + self.alternative_hypothesis,
"Distribution Order: " + str(self.distribution_order),
]
smry.add_extra_txt(extra_text)
return smry
def summary(self, yname=None, xname=None, title=0, alpha=.05,
return_fmt='text'):
"""
This is for testing the new summary setup
"""
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.fit_history['iteration']])
]
top_right = [('No. Observations:', None),
('Df Residuals:', None),
('Df Model:', None)
]
if title is not None:
title = "Robust linear Model Regression Results"
# boiler plate
from 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=alpha,
use_t=self.use_t)
# add warnings/notes, added to text format only
etext = []
wstr = ("If the model instance has been used for another fit "
"with different fit\n"
"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):
"""Summary of test, containing statistic, p-value and critical values
"""
table_data = [('Test Statistic', '{0:0.3f}'.format(self.stat)),
('P-value', '{0:0.3f}'.format(self.pvalue)),
('Lags', '{0:d}'.format(self.lags))]
title = self._title
if not title:
title = self._test_name + " Results"
table = SimpleTable(table_data,
stubs=None,
title=title,
colwidths=18,
datatypes=[0, 1],
data_aligns=("l", "r"))
smry = Summary()
smry.tables.append(table)
cv_string = 'Critical Values: '
cv = self._critical_values.keys()
g = lambda x: float(x.split('%')[0])
cv_numeric = array(lmap(g, cv))
cv_numeric = sort(cv_numeric)
for val in cv_numeric:
p = str(int(val)) + '%'
cv_string += '{0:0.2f}'.format(self._critical_values[p])
cv_string += ' (' + p + ')'
if val != cv_numeric[-1]:
cv_string += ', '
extra_text = [
'Trend: ' + TREND_DESCRIPTION[self._trend], cv_string,
'Null Hypothesis: ' + self.null_hypothesis,
'Alternative Hypothesis: ' + self.alternative_hypothesis
]
smry.add_extra_txt(extra_text)
if self._summary_text:
smry.add_extra_txt(self._summary_text)
return smry
def summary(self):
"""Summary of test, containing statistic, p-value and critical values
"""
table_data = [('Test Statistic', '{0:0.3f}'.format(self.stat)),
('P-value', '{0:0.3f}'.format(self.pvalue)),
('Lags', '{0:d}'.format(self.lags))]
title = self._title
if not title:
title = self._test_name + " Results"
table = SimpleTable(table_data, stubs=None, title=title, colwidths=18,
datatypes=[0, 1], data_aligns=("l", "r"))
smry = Summary()
smry.tables.append(table)
cv_string = 'Critical Values: '
cv = self._critical_values.keys()
g = lambda x: float(x.split('%')[0])
cv_numeric = array(lmap(g, cv))
cv_numeric = sort(cv_numeric)
for val in cv_numeric:
p = str(int(val)) + '%'
cv_string += '{0:0.2f}'.format(self._critical_values[p])
cv_string += ' (' + p + ')'
if val != cv_numeric[-1]:
cv_string += ', '
extra_text = ['Trend: ' + TREND_DESCRIPTION[self._trend],
cv_string,
'Null Hypothesis: ' + self.null_hypothesis,
'Alternative Hypothesis: ' + self.alternative_hypothesis]
smry.add_extra_txt(extra_text)
if self._summary_text:
smry.add_extra_txt(self._summary_text)
return smry
def summary(self):
"""
Constructs a summary of the results from a fit model.
Returns
-------
summary : Summary instance
Object that contains tables and facilitated export to text, html or
latex
"""
# Summary layout
# 1. Overall information
# 2. Mean parameters
# 3. Volatility parameters
# 4. Distribution parameters
# 5. Notes
model = self.model
model_name = model.name + ' - ' + model.volatility.name
# Summary Header
top_left = [('Dep. Variable:', self._dep_name),
('Mean Model:', model.name),
('Vol Model:', model.volatility.name),
('Distribution:', model.distribution.name),
('Method:', 'Maximum Likelihood'), ('', ''),
('Date:', self._datetime.strftime('%a, %b %d %Y')),
('Time:', self._datetime.strftime('%H:%M:%S'))]
top_right = [('R-squared:', '%#8.3f' % self.rsquared),
('Adj. R-squared:', '%#8.3f' % self.rsquared_adj),
('Log-Likelihood:', '%#10.6g' % self.loglikelihood),
('AIC:', '%#10.6g' % self.aic),
('BIC:', '%#10.6g' % self.bic),
('No. Observations:', self._nobs),
('Df Residuals:', self.nobs - self.num_params),
('Df Model:', self.num_params)]
title = model_name + ' Model Results'
stubs = []
vals = []
for stub, val in top_left:
stubs.append(stub)
vals.append([val])
table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)
# create summary table instance
smry = Summary()
# Top Table
# Parameter table
fmt = fmt_2cols
fmt['data_fmts'][1] = '%18s'
top_right = [('%-21s' % (' ' + k), v) for k, v in top_right]
stubs = []
vals = []
for stub, val in top_right:
stubs.append(stub)
vals.append([val])
table.extend_right(SimpleTable(vals, stubs=stubs))
smry.tables.append(table)
conf_int = np.asarray(self.conf_int())
conf_int_str = []
for c in conf_int:
conf_int_str.append('[' + format_float_fixed(c[0], 7, 3) + ',' +
format_float_fixed(c[1], 7, 3) + ']')
stubs = self._names
header = ['coef', 'std err', 't', 'P>|t|', '95.0% Conf. Int.']
vals = (self.params, self.std_err, self.tvalues, self.pvalues,
conf_int_str)
formats = [(10, 4), (9, 3), (9, 3), (9, 3), None]
pos = 0
param_table_data = []
for _ in range(len(vals[0])):
row = []
for i, val in enumerate(vals):
if isinstance(val[pos], np.float64):
converted = format_float_fixed(val[pos], *formats[i])
else:
converted = val[pos]
row.append(converted)
pos += 1
param_table_data.append(row)
mc = self.model.num_params
vc = self.model.volatility.num_params
dc = self.model.distribution.num_params
counts = (mc, vc, dc)
titles = ('Mean Model', 'Volatility Model', 'Distribution')
total = 0
for title, count in zip(titles, counts):
if count == 0:
continue
table_data = param_table_data[total:total + count]
table_stubs = stubs[total:total + count]
total += count
table = SimpleTable(table_data,
stubs=table_stubs,
txt_fmt=fmt_params,
headers=header,
title=title)
smry.tables.append(table)
extra_text = ['Covariance estimator: ' + self.cov_type]
if self.convergence_flag:
extra_text.append("""
WARNING: The optimizer did not indicate sucessful convergence. The message was
{string_message}. See convergence_flag.""".format(
string_message=self._optim_output[-1]))
smry.add_extra_txt(extra_text)
return smry
def summary(self):
"""Summary table of model comparison"""
smry = Summary()
models = list(self._results.keys())
title = 'Model Comparison'
stubs = [
'Dep. Variable', 'Estimator', 'No. Observations', 'Cov. Est.',
'R-squared', 'Adj. R-squared', 'F-statistic', 'P-value (F-stat)'
]
dep_name = {}
for key in self._results:
dep_name[key] = self._results[key].model.dependent.cols[0]
dep_name = Series(dep_name)
vals = concat([
dep_name, self.estimator_method, self.nobs, self.cov_estimator,
self.rsquared, self.rsquared_adj, self.f_statistic
], 1)
vals = [[i for i in v] for v in vals.T.values]
vals[2] = [str(v) for v in vals[2]]
for i in range(4, len(vals)):
vals[i] = [_str(v) for v in vals[i]]
params = self.params
tstats = self.tstats
params_fmt = []
params_stub = []
for i in range(len(params)):
params_fmt.append([_str(v) for v in params.values[i]])
tstats_fmt = []
for v in tstats.values[i]:
v_str = _str(v)
v_str = '({0})'.format(v_str) if v_str.strip() else v_str
tstats_fmt.append(v_str)
params_fmt.append(tstats_fmt)
params_stub.append(params.index[i])
params_stub.append(' ')
vals = table_concat((vals, params_fmt))
stubs = stub_concat((stubs, params_stub))
all_instr = []
for key in self._results:
res = self._results[key]
all_instr.append(res.model.instruments.cols)
ninstr = max(map(lambda l: len(l), all_instr))
instruments = []
instrument_stub = ['Instruments']
for i in range(ninstr):
if i > 0:
instrument_stub.append('')
row = []
for j in range(len(self._results)):
instr = all_instr[j]
if len(instr) > i:
row.append(instr[i])
else:
row.append('')
instruments.append(row)
if instruments:
vals = table_concat((vals, instruments))
stubs = stub_concat((stubs, instrument_stub))
txt_fmt = default_txt_fmt.copy()
txt_fmt['data_aligns'] = 'r'
txt_fmt['header_align'] = 'r'
table = SimpleTable(vals,
headers=models,
title=title,
stubs=stubs,
txt_fmt=txt_fmt)
smry.tables.append(table)
smry.add_extra_txt(['T-stats reported in parentheses'])
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
--------
statsmodels.iolib.summary.Summary : class to hold summary results
"""
eigvals = self.eigenvals
condno = self.condition_number
top_left = [('Dep. Variable:', None),
('Model:', None),
('Method:', ['Least Squares']),
('Date:', None),
('Time:', None)
]
top_right = [('Pseudo R-squared:', ["%#8.4g" % self.prsquared]),
('Bandwidth:', ["%#8.4g" % self.bandwidth]),
('Sparsity:', ["%#8.4g" % self.sparsity]),
('No. Observations:', None),
('Df Residuals:', None),
('Df Model:', None)
]
if title is None:
title = self.model.__class__.__name__ + ' ' + "Regression Results"
# create summary table instance
from 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=alpha,
use_t=self.use_t)
# add warnings/notes, added to text format only
etext = []
if eigvals[-1] < 1e-10:
wstr = "The smallest eigenvalue is %6.3g. This might indicate "
wstr += "that there are\n"
wstr += "strong multicollinearity problems or that the design "
wstr += "matrix is singular."
wstr = wstr % eigvals[-1]
etext.append(wstr)
elif condno > 1000: # TODO: what is recommended
wstr = "The condition number is large, %6.3g. This might "
wstr += "indicate that there are\n"
wstr += "strong multicollinearity or other numerical "
wstr += "problems."
wstr = wstr % condno
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
--------
statsmodels.iolib.summary.Summary : class to hold summary
results
"""
# #TODO: import where we need it (for now), add as cached attributes
# from statsmodels.stats.stattools import (jarque_bera,
# omni_normtest, durbin_watson)
# jb, jbpv, skew, kurtosis = jarque_bera(self.wresid)
# omni, omnipv = omni_normtest(self.wresid)
#
eigvals = self.eigenvals
condno = self.condition_number
#
# self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis,
# omni=omni, omnipv=omnipv, condno=condno,
# mineigval=eigvals[0])
top_left = [('Dep. Variable:', None), ('Model:', None),
('Method:', ['Least Squares']), ('Date:', None),
('Time:', None)]
top_right = [
('Pseudo R-squared:', ["%#8.4g" % self.prsquared]),
('Bandwidth:', ["%#8.4g" % self.bandwidth]),
('Sparsity:', ["%#8.4g" % self.sparsity]),
('No. Observations:', None),
('Df Residuals:', None), #[self.df_resid]), #TODO: spelling
('Df Model:', None) #[self.df_model])
]
# diagn_left = [('Omnibus:', ["%#6.3f" % omni]),
# ('Prob(Omnibus):', ["%#6.3f" % omnipv]),
# ('Skew:', ["%#6.3f" % skew]),
# ('Kurtosis:', ["%#6.3f" % kurtosis])
# ]
#
# diagn_right = [('Durbin-Watson:', ["%#8.3f" % durbin_watson(self.wresid)]),
# ('Jarque-Bera (JB):', ["%#8.3f" % jb]),
# ('Prob(JB):', ["%#8.3g" % jbpv]),
# ('Cond. No.', ["%#8.3g" % condno])
# ]
if title is None:
title = self.model.__class__.__name__ + ' ' + "Regression Results"
#create summary table instance
from 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)
# 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 = []
if eigvals[-1] < 1e-10:
wstr = "The smallest eigenvalue is %6.3g. This might indicate "
wstr += "that there are\n"
wstr += "strong multicollinearity problems or that the design "
wstr += "matrix is singular."
wstr = wstr % eigvals[-1]
etext.append(wstr)
elif condno > 1000: #TODO: what is recommended
wstr = "The condition number is large, %6.3g. This might "
wstr += "indicate that there are\n"
wstr += "strong multicollinearity or other numerical "
wstr += "problems."
wstr = wstr % condno
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
--------
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.fit_history['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 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=alpha,
use_t=self.use_t)
if hasattr(self, 'constraints'):
smry.add_extra_txt(['Model has been estimated subject to linear '
'equality constraints.'])
#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,
alpha=0.05,
start=None,
title=None,
model_name=None,
display_params=True,
):
"""
Summarize the Model
Parameters
----------
alpha : float, optional
Significance level for the confidence intervals. Default is 0.05.
start : int, optional
Integer of the start observation. Default is 0.
model_name : str
The name of the model used. Default is to use model class name.
Returns
-------
summary : Summary instance
This holds the summary table and text, which can be printed or
converted to various output formats.
See Also
--------
statsmodels.iolib.summary.Summary
"""
from statsmodels.iolib.summary import Summary
# Model specification results
model = self.model
if title is None:
title = "Statespace Model Results"
if start is None:
start = 0
if self.model._index_dates:
ix = self.model._index
d = ix[start]
sample = ["%02d-%02d-%02d" % (d.month, d.day, d.year)]
d = ix[-1]
sample += ["- " + "%02d-%02d-%02d" % (d.month, d.day, d.year)]
else:
sample = [str(start), " - " + str(self.nobs)]
# Standardize the model name as a list of str
if model_name is None:
model_name = model.__class__.__name__
# Diagnostic tests results
try:
het = self.test_heteroskedasticity(method="breakvar")
except Exception: # FIXME: catch something specific
het = np.array([[np.nan] * 2])
try:
lb = self.test_serial_correlation(method="ljungbox")
except Exception: # FIXME: catch something specific
lb = np.array([[np.nan] * 2]).reshape(1, 2, 1)
try:
jb = self.test_normality(method="jarquebera")
except Exception: # FIXME: catch something specific
jb = np.array([[np.nan] * 4])
# Create the tables
if not isinstance(model_name, list):
model_name = [model_name]
top_left = [("Dep. Variable:", None)]
top_left.append(("Model:", [model_name[0]]))
for i in range(1, len(model_name)):
top_left.append(("", ["+ " + model_name[i]]))
top_left += [
("Date:", None),
("Time:", None),
("Sample:", [sample[0]]),
("", [sample[1]]),
]
top_right = [
("No. Observations:", [self.nobs]),
("Log Likelihood", ["%#5.3f" % self.llf]),
]
if hasattr(self, "rsquared"):
top_right.append(("R-squared:", ["%#8.3f" % self.rsquared]))
top_right += [
("AIC", ["%#5.3f" % self.aic]),
("BIC", ["%#5.3f" % self.bic]),
("HQIC", ["%#5.3f" % self.hqic]),
]
if hasattr(self, "filter_results"):
if (self.filter_results is not None
and self.filter_results.filter_concentrated):
top_right.append(("Scale", ["%#5.3f" % self.scale]))
else:
top_right.append(("Scale", ["%#5.3f" % self.scale]))
if hasattr(self, "cov_type"):
top_left.append(("Covariance Type:", [self.cov_type]))
format_str = lambda array: [ # noqa:E731
", ".join(["{0:.2f}".format(i) for i in array])
]
diagn_left = [
("Ljung-Box (Q):", format_str(lb[:, 0, -1])),
("Prob(Q):", format_str(lb[:, 1, -1])),
("Heteroskedasticity (H):", format_str(het[:, 0])),
("Prob(H) (two-sided):", format_str(het[:, 1])),
]
diagn_right = [
("Jarque-Bera (JB):", format_str(jb[:, 0])),
("Prob(JB):", format_str(jb[:, 1])),
("Skew:", format_str(jb[:, 2])),
("Kurtosis:", format_str(jb[:, 3])),
]
summary = Summary()
summary.add_table_2cols(self,
gleft=top_left,
gright=top_right,
title=title)
if len(self.params) > 0 and display_params:
summary.add_table_params(self,
alpha=alpha,
xname=self.param_names,
use_t=False)
summary.add_table_2cols(self,
gleft=diagn_left,
gright=diagn_right,
title="")
# Add warnings/notes, added to text format only
etext = []
if hasattr(self, "cov_type") and "description" in self.cov_kwds:
etext.append(self.cov_kwds["description"])
if self._rank < (len(self.params) - len(self.fixed_params)):
cov_params = self.cov_params()
if len(self.fixed_params) > 0:
mask = np.ix_(self._free_params_index, self._free_params_index)
cov_params = cov_params[mask]
etext.append("Covariance matrix is singular or near-singular,"
" with condition number %6.3g. Standard errors may be"
" unstable." % _safe_cond(cov_params))
if etext:
etext = [
"[{0}] {1}".format(i + 1, text) for i, text in enumerate(etext)
]
etext.insert(0, "Warnings:")
summary.add_extra_txt(etext)
return summary
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
--------
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.fit_history['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 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=alpha,
use_t=self.use_t)
if hasattr(self, 'constraints'):
smry.add_extra_txt([
'Model has been estimated subject to linear '
'equality constraints.'
])
#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):
"""
Constructs a summary of the results from a fit model.
Returns
-------
summary : Summary instance
Object that contains tables and facilitated export to text, html or
latex
"""
# Summary layout
# 1. Overall information
# 2. Mean parameters
# 3. Volatility parameters
# 4. Distribution parameters
# 5. Notes
model = self.model
model_name = model.name + " - " + model.volatility.name
# Summary Header
top_left = [
("Dep. Variable:", self._dep_name),
("Mean Model:", model.name),
("Vol Model:", model.volatility.name),
("Distribution:", model.distribution.name),
("Method:", "User-specified Parameters"),
("", ""),
("Date:", self._datetime.strftime("%a, %b %d %Y")),
("Time:", self._datetime.strftime("%H:%M:%S")),
]
top_right = [
("R-squared:", "--"),
("Adj. R-squared:", "--"),
("Log-Likelihood:", "%#10.6g" % self.loglikelihood),
("AIC:", "%#10.6g" % self.aic),
("BIC:", "%#10.6g" % self.bic),
("No. Observations:", self._nobs),
("", ""),
("", ""),
]
title = model_name + " Model Results"
stubs = []
vals = []
for stub, val in top_left:
stubs.append(stub)
vals.append([val])
table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)
# create summary table instance
smry = Summary()
# Top Table
# Parameter table
fmt = fmt_2cols
fmt["data_fmts"][1] = "%18s"
top_right = [("%-21s" % (" " + k), v) for k, v in top_right]
stubs = []
vals = []
for stub, val in top_right:
stubs.append(stub)
vals.append([val])
table.extend_right(SimpleTable(vals, stubs=stubs))
smry.tables.append(table)
stubs = self._names
header = ["coef"]
vals = (self.params,)
formats = [(10, 4)]
pos = 0
param_table_data = []
for _ in range(len(vals[0])):
row = []
for i, val in enumerate(vals):
if isinstance(val[pos], np.float64):
converted = format_float_fixed(val[pos], *formats[i])
else:
converted = val[pos]
row.append(converted)
pos += 1
param_table_data.append(row)
mc = self.model.num_params
vc = self.model.volatility.num_params
dc = self.model.distribution.num_params
counts = (mc, vc, dc)
titles = ("Mean Model", "Volatility Model", "Distribution")
total = 0
for title, count in zip(titles, counts):
if count == 0:
continue
table_data = param_table_data[total : total + count]
table_stubs = stubs[total : total + count]
total += count
table = SimpleTable(table_data, stubs=table_stubs, txt_fmt=fmt_params, headers=header, title=title)
smry.tables.append(table)
extra_text = (
"Results generated with user-specified parameters.",
"Since the model was not estimated, there are no std. " "errors.",
)
smry.add_extra_txt(extra_text)
return smry
def summary(self):
"""Summary table of model estimation results"""
title = self._method + ' Estimation Summary'
mod = self.model
top_left = [('Dep. Variable:', mod.dependent.cols[0]),
('Estimator:', self._method),
('No. Observations:', self.nobs),
('Date:', self._datetime.strftime('%a, %b %d %Y')),
('Time:', self._datetime.strftime('%H:%M:%S')),
('Cov. Estimator:', self._cov_type), ('', '')]
top_right = [('R-squared:', _str(self.rsquared)),
('Adj. R-squared:', _str(self.rsquared_adj)),
('F-statistic:', _str(self.f_statistic.stat)),
('P-value (F-stat)', pval_format(self.f_statistic.pval)),
('Distribution:', str(self.f_statistic.dist_name)),
('', ''), ('', '')]
stubs = []
vals = []
for stub, val in top_left:
stubs.append(stub)
vals.append([val])
table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)
# create summary table instance
smry = Summary()
# Top Table
# Parameter table
fmt = fmt_2cols
fmt['data_fmts'][1] = '%18s'
top_right = [('%-21s' % (' ' + k), v) for k, v in top_right]
stubs = []
vals = []
for stub, val in top_right:
stubs.append(stub)
vals.append([val])
table.extend_right(SimpleTable(vals, stubs=stubs))
smry.tables.append(table)
param_data = c_[self.params.values[:, None],
self.std_errors.values[:, None],
self.tstats.values[:, None], self.pvalues.values[:,
None],
self.conf_int()]
data = []
for row in param_data:
txt_row = []
for i, v in enumerate(row):
f = _str
if i == 3:
f = pval_format
txt_row.append(f(v))
data.append(txt_row)
title = 'Parameter Estimates'
table_stubs = list(self.params.index)
header = [
'Parameters', 'Std. Err.', 'T-stat', 'P-value', 'Lower CI',
'Upper CI'
]
table = SimpleTable(data,
stubs=table_stubs,
txt_fmt=fmt_params,
headers=header,
title=title)
smry.tables.append(table)
instruments = self.model.instruments
if instruments.shape[1] > 0:
extra_text = []
endog = self.model.endog
extra_text.append('Endogenous: ' + ', '.join(endog.cols))
extra_text.append('Instruments: ' + ', '.join(instruments.cols))
cov_descr = str(self._cov_estimator)
for line in cov_descr.split('\n'):
extra_text.append(line)
smry.add_extra_txt(extra_text)
return smry
def summary(self, yname=None, xname=None, title=None, alpha=.05):
"""Summarize the Regression Results
Parameters
----------
yname : str, optional
Default is `y`
xname : list[str], optional
Names for the exogenous variables. Default is `var_##` for ## in
the number of regressors. Must match the number of parameters
in the model
title : str, 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
--------
statsmodels.iolib.summary.Summary : class to hold summary results
"""
eigvals = self.eigenvals
condno = self.condition_number
top_left = [('Dep. Variable:', None),
('Model:', None),
('Method:', ['Least Squares']),
('Date:', None),
('Time:', None)
]
top_right = [('Pseudo R-squared:', ["%#8.4g" % self.prsquared]),
('Bandwidth:', ["%#8.4g" % self.bandwidth]),
('Sparsity:', ["%#8.4g" % self.sparsity]),
('No. Observations:', None),
('Df Residuals:', None),
('Df Model:', None)
]
if title is None:
title = self.model.__class__.__name__ + ' ' + "Regression Results"
# create summary table instance
from 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=alpha,
use_t=self.use_t)
# add warnings/notes, added to text format only
etext = []
if eigvals[-1] < 1e-10:
wstr = "The smallest eigenvalue is %6.3g. This might indicate "
wstr += "that there are\n"
wstr += "strong multicollinearity problems or that the design "
wstr += "matrix is singular."
wstr = wstr % eigvals[-1]
etext.append(wstr)
elif condno > 1000: # TODO: what is recommended
wstr = "The condition number is large, %6.3g. This might "
wstr += "indicate that there are\n"
wstr += "strong multicollinearity or other numerical "
wstr += "problems."
wstr = wstr % condno
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
def summary(self):
"""
Constructs a summary of the results from a fit model.
Returns
-------
summary : Summary instance
Object that contains tables and facilitated export to text, html or
latex
"""
# Summary layout
# 1. Overall information
# 2. Mean parameters
# 3. Volatility parameters
# 4. Distribution parameters
# 5. Notes
model = self.model
model_name = model.name + ' - ' + model.volatility.name
# Summary Header
top_left = [('Dep. Variable:', self._dep_name),
('Mean Model:', model.name),
('Vol Model:', model.volatility.name),
('Distribution:', model.distribution.name),
('Method:', 'User-specified Parameters'), ('', ''),
('Date:', self._datetime.strftime('%a, %b %d %Y')),
('Time:', self._datetime.strftime('%H:%M:%S'))]
top_right = [
('R-squared:', '--'),
('Adj. R-squared:', '--'),
('Log-Likelihood:', '%#10.6g' % self.loglikelihood),
('AIC:', '%#10.6g' % self.aic),
('BIC:', '%#10.6g' % self.bic),
('No. Observations:', self._nobs),
('', ''),
('', ''),
]
title = model_name + ' Model Results'
stubs = []
vals = []
for stub, val in top_left:
stubs.append(stub)
vals.append([val])
table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)
# create summary table instance
smry = Summary()
# Top Table
# Parameter table
fmt = fmt_2cols
fmt['data_fmts'][1] = '%18s'
top_right = [('%-21s' % (' ' + k), v) for k, v in top_right]
stubs = []
vals = []
for stub, val in top_right:
stubs.append(stub)
vals.append([val])
table.extend_right(SimpleTable(vals, stubs=stubs))
smry.tables.append(table)
stubs = self._names
header = ['coef']
vals = (self.params, )
formats = [(10, 4)]
pos = 0
param_table_data = []
for _ in range(len(vals[0])):
row = []
for i, val in enumerate(vals):
if isinstance(val[pos], np.float64):
converted = format_float_fixed(val[pos], *formats[i])
else:
converted = val[pos]
row.append(converted)
pos += 1
param_table_data.append(row)
mc = self.model.num_params
vc = self.model.volatility.num_params
dc = self.model.distribution.num_params
counts = (mc, vc, dc)
titles = ('Mean Model', 'Volatility Model', 'Distribution')
total = 0
for title, count in zip(titles, counts):
if count == 0:
continue
table_data = param_table_data[total:total + count]
table_stubs = stubs[total:total + count]
total += count
table = SimpleTable(table_data,
stubs=table_stubs,
txt_fmt=fmt_params,
headers=header,
title=title)
smry.tables.append(table)
extra_text = ('Results generated with user-specified parameters.',
'Since the model was not estimated, there are no std. '
'errors.')
smry.add_extra_txt(extra_text)
return smry
def summary(self, alpha=.05, start=None, model_name=None):
"""
Summarize the Model
Parameters
----------
alpha : float, optional
Significance level for the confidence intervals. Default is 0.05.
start : int, optional
Integer of the start observation. Default is 0.
model_name : string
The name of the model used. Default is to use model class name.
Returns
-------
summary : Summary instance
This holds the summary table and text, which can be printed or
converted to various output formats.
See Also
--------
statsmodels.iolib.summary.Summary
"""
from statsmodels.iolib.summary import Summary
model = self.model
title = 'Statespace Model Results'
if start is None:
start = 0
if self.data.dates is not None:
dates = self.data.dates
d = dates[start]
sample = ['%02d-%02d-%02d' % (d.month, d.day, d.year)]
d = dates[-1]
sample += ['- ' + '%02d-%02d-%02d' % (d.month, d.day, d.year)]
else:
sample = [str(start), ' - ' + str(self.model.nobs)]
if model_name is None:
model_name = model.__class__.__name__
top_left = [('Dep. Variable:', None), ('Model:', [model_name]),
('Date:', None), ('Time:', None), ('Sample:', [sample[0]]),
('', [sample[1]])]
top_right = [('No. Observations:', [self.model.nobs]),
('Log Likelihood', ["%#5.3f" % self.llf]),
('AIC', ["%#5.3f" % self.aic]),
('BIC', ["%#5.3f" % self.bic]),
('HQIC', ["%#5.3f" % self.hqic])]
if hasattr(self, 'cov_type'):
top_left.append(('Covariance Type:', [self.cov_type]))
summary = Summary()
summary.add_table_2cols(self,
gleft=top_left,
gright=top_right,
title=title)
summary.add_table_params(self,
alpha=alpha,
xname=self._param_names,
use_t=False)
# Add warnings/notes, added to text format only
etext = []
if hasattr(self, 'cov_type'):
etext.append(self.cov_kwds['description'])
if etext:
etext = [
"[{0}] {1}".format(i + 1, text) for i, text in enumerate(etext)
]
etext.insert(0, "Warnings:")
summary.add_extra_txt(etext)
return summary
def summary(self):
"""
Constructs a summary of the results from a fit model.
Returns
-------
summary : Summary instance
Object that contains tables and facilitated export to text, html or
latex
"""
# Summary layout
# 1. Overall information
# 2. Mean parameters
# 3. Volatility parameters
# 4. Distribution parameters
# 5. Notes
model = self.model
model_name = model.name + ' - ' + model.volatility.name
# Summary Header
top_left = [('Dep. Variable:', self._dep_name),
('Mean Model:', model.name),
('Vol Model:', model.volatility.name),
('Distribution:', model.distribution.name),
('Method:', 'Maximum Likelihood'),
('', ''),
('Date:', self._datetime.strftime('%a, %b %d %Y')),
('Time:', self._datetime.strftime('%H:%M:%S'))]
top_right = [('R-squared:', '%#8.3f' % self.rsquared),
('Adj. R-squared:', '%#8.3f' % self.rsquared_adj),
('Log-Likelihood:', '%#10.6g' % self.loglikelihood),
('AIC:', '%#10.6g' % self.aic),
('BIC:', '%#10.6g' % self.bic),
('No. Observations:', self._nobs),
('Df Residuals:', self.nobs - self.num_params),
('Df Model:', self.num_params)]
title = model_name + ' Model Results'
stubs = []
vals = []
for stub, val in top_left:
stubs.append(stub)
vals.append([val])
table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)
# create summary table instance
smry = Summary()
# Top Table
# Parameter table
fmt = fmt_2cols
fmt['data_fmts'][1] = '%18s'
top_right = [('%-21s' % (' ' + k), v) for k, v in top_right]
stubs = []
vals = []
for stub, val in top_right:
stubs.append(stub)
vals.append([val])
table.extend_right(SimpleTable(vals, stubs=stubs))
smry.tables.append(table)
conf_int = np.asarray(self.conf_int())
conf_int_str = []
for c in conf_int:
conf_int_str.append('[' + format_float_fixed(c[0], 7, 3)
+ ',' + format_float_fixed(c[1], 7, 3) + ']')
stubs = self._names
header = ['coef', 'std err', 't', 'P>|t|', '95.0% Conf. Int.']
vals = (self.params,
self.std_err,
self.tvalues,
self.pvalues,
conf_int_str)
formats = [(10, 4), (9, 3), (9, 3), (9, 3), None]
pos = 0
param_table_data = []
for _ in range(len(vals[0])):
row = []
for i, val in enumerate(vals):
if isinstance(val[pos], np.float64):
converted = format_float_fixed(val[pos], *formats[i])
else:
converted = val[pos]
row.append(converted)
pos += 1
param_table_data.append(row)
mc = self.model.num_params
vc = self.model.volatility.num_params
dc = self.model.distribution.num_params
counts = (mc, vc, dc)
titles = ('Mean Model', 'Volatility Model', 'Distribution')
total = 0
for title, count in zip(titles, counts):
if count == 0:
continue
table_data = param_table_data[total:total + count]
table_stubs = stubs[total:total + count]
total += count
table = SimpleTable(table_data,
stubs=table_stubs,
txt_fmt=fmt_params,
headers=header, title=title)
smry.tables.append(table)
extra_text = ['Covariance estimator: ' + self.cov_type]
if self.convergence_flag:
extra_text.append("""
WARNING: The optimizer did not indicate sucessful convergence. The message was
{string_message}. See convergence_flag.""".format(
string_message=self._optim_output[-1]))
smry.add_extra_txt(extra_text)
return smry
def summary(self):
"""
Constructs a summary of the results from a fit model.
Returns
-------
summary : Summary instance
Object that contains tables and facilitated export to text, html or
latex
"""
# Summary layout
# 1. Overall information
# 2. Mean parameters
# 3. Volatility parameters
# 4. Distribution parameters
# 5. Notes
model = self.model
model_name = model.name + ' - ' + model.volatility.name
# Summary Header
top_left = [('Dep. Variable:', self._dep_name),
('Mean Model:', model.name),
('Vol Model:', model.volatility.name),
('Distribution:', model.distribution.name),
('Method:', 'User-specified Parameters'),
('', ''),
('Date:', self._datetime.strftime('%a, %b %d %Y')),
('Time:', self._datetime.strftime('%H:%M:%S'))]
top_right = [('R-squared:', '--'),
('Adj. R-squared:', '--'),
('Log-Likelihood:', '%#10.6g' % self.loglikelihood),
('AIC:', '%#10.6g' % self.aic),
('BIC:', '%#10.6g' % self.bic),
('No. Observations:', self._nobs),
('', ''),
('', ''),]
title = model_name + ' Model Results'
stubs = []
vals = []
for stub, val in top_left:
stubs.append(stub)
vals.append([val])
table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)
# create summary table instance
smry = Summary()
# Top Table
# Parameter table
fmt = fmt_2cols
fmt['data_fmts'][1] = '%18s'
top_right = [('%-21s' % (' ' + k), v) for k, v in top_right]
stubs = []
vals = []
for stub, val in top_right:
stubs.append(stub)
vals.append([val])
table.extend_right(SimpleTable(vals, stubs=stubs))
smry.tables.append(table)
stubs = self._names
header = ['coef']
vals = (self.params,)
formats = [(10, 4)]
pos = 0
param_table_data = []
for _ in range(len(vals[0])):
row = []
for i, val in enumerate(vals):
if isinstance(val[pos], np.float64):
converted = format_float_fixed(val[pos], *formats[i])
else:
converted = val[pos]
row.append(converted)
pos += 1
param_table_data.append(row)
mc = self.model.num_params
vc = self.model.volatility.num_params
dc = self.model.distribution.num_params
counts = (mc, vc, dc)
titles = ('Mean Model', 'Volatility Model', 'Distribution')
total = 0
for title, count in zip(titles, counts):
if count == 0:
continue
table_data = param_table_data[total:total + count]
table_stubs = stubs[total:total + count]
total += count
table = SimpleTable(table_data,
stubs=table_stubs,
txt_fmt=fmt_params,
headers=header, title=title)
smry.tables.append(table)
extra_text = ('Results generated with user-specified parameters.',
'Since the model was not estimated, there are no std. '
'errors.')
smry.add_extra_txt(extra_text)
return smry
def summary(self, alpha=.05, start=None, model_name=None):
"""
Summarize the Model
Parameters
----------
alpha : float, optional
Significance level for the confidence intervals. Default is 0.05.
start : int, optional
Integer of the start observation. Default is 0.
model_name : string
The name of the model used. Default is to use model class name.
Returns
-------
summary : Summary instance
This holds the summary table and text, which can be printed or
converted to various output formats.
See Also
--------
statsmodels.iolib.summary.Summary
"""
from statsmodels.iolib.summary import Summary
model = self.model
title = 'Statespace Model Results'
if start is None:
start = 0
if self.data.dates is not None:
dates = self.data.dates
d = dates[start]
sample = ['%02d-%02d-%02d' % (d.month, d.day, d.year)]
d = dates[-1]
sample += ['- ' + '%02d-%02d-%02d' % (d.month, d.day, d.year)]
else:
sample = [str(start), ' - ' + str(self.model.nobs)]
if model_name is None:
model_name = model.__class__.__name__
top_left = [
('Dep. Variable:', None),
('Model:', [model_name]),
('Date:', None),
('Time:', None),
('Sample:', [sample[0]]),
('', [sample[1]])
]
top_right = [
('No. Observations:', [self.model.nobs]),
('Log Likelihood', ["%#5.3f" % self.llf]),
('AIC', ["%#5.3f" % self.aic]),
('BIC', ["%#5.3f" % self.bic]),
('HQIC', ["%#5.3f" % self.hqic])
]
if hasattr(self, 'cov_type'):
top_left.append(('Covariance Type:', [self.cov_type]))
summary = Summary()
summary.add_table_2cols(self, gleft=top_left, gright=top_right,
title=title)
summary.add_table_params(self, alpha=alpha,
xname=self.data.param_names, use_t=False)
# Add warnings/notes, added to text format only
etext = []
if hasattr(self, 'cov_type'):
etext.append(self.cov_kwds['description'])
if etext:
etext = ["[{0}] {1}".format(i + 1, text)
for i, text in enumerate(etext)]
etext.insert(0, "Warnings:")
summary.add_extra_txt(etext)
return summary
def summary(self, regpyhdfe, yname=None, xname=None, title=None, alpha=.05):
"""
Summarize the Regression Results.
Parameters
----------
yname : str, optional
Name of endogenous (response) variable. The Default is `y`.
xname : list[str], optional
Names for the exogenous variables. Default is `var_##` for ## in
the number of regressors. Must match the number of parameters
in the model.
title : str, optional
Title for the top table. If not None, then this replaces the
default title.
alpha : float
The significance level for the confidence intervals.
Returns
-------
Summary
Instance holding the summary tables and text, which can be printed
or converted to various output formats.
See Also
--------
statsmodels.iolib.summary.Summary : A class that holds summary results.
"""
##########################################################################################################
##########################################################################################################
# https://apithymaxim.wordpress.com/2020/03/16/clustering-standard-errors-by-hand-using-python/
# http://cameron.econ.ucdavis.edu/research/Cameron_Miller_JHR_2015_February.pdf
#N,k,Nclusts = len(df.index),3,50 # Number of observations, right hand side columns counting constant, number of clusters
#X = np.hstack( (np.random.random((N,k-1)), np.ones((N,1)) ) )
#X = get_np_columns(df, ['wks_ue', 'tenure'], intercept=True)
X = regpyhdfe.data[:, 1:]
#y = get_np_columns(df, ['ttl_exp'])
y = np.expand_dims(regpyhdfe.data[:, 0], 1)
# Calculate (X'X)^-1 and the vector of coefficients, beta
XX_inv = np.linalg.inv(X.T.dot(X))
beta = (XX_inv).dot(X.T.dot(y))
resid = y - X.dot(beta)
#ID = np.random.choice([x for x in range(Nclusts)],N) # Vector of cluster IDs
#ID = np.squeeze(get_np_columns(df, ['delete_me']))
ID = np.squeeze(regpyhdfe.groups_np)
c_list = np.unique(ID) # Get unique list of clusters
N, k, Nclusts = X.shape[0], X.shape[1], int(c_list.shape[0])
sum_XuuTX = 0
for c in range(0, Nclusts):
in_cluster = (ID == c_list[c]) # Indicator for given cluster value
resid_c = resid[in_cluster]
uuT = resid_c.dot(resid_c.T)
Xc = X[in_cluster]
XuuTX = Xc.T.dot(uuT).dot(Xc)
sum_XuuTX += XuuTX
adj = (Nclusts / (Nclusts - 1)) * (
(N - 1) / (N - k)
) # Degrees of freedom correction from https://www.stata.com/manuals13/u20.pdf p. 54
# TODO: actually check if the fixed effects are nested
df_a_nested = 1
adj = ((N - 1) / (N - df_a_nested - k)) * (Nclusts / (Nclusts - 1))
V_beta = adj * (XX_inv.dot(sum_XuuTX).dot(XX_inv))
se_beta = np.sqrt(np.diag(V_beta))
# Output data for Stata
for_stata = pd.DataFrame(X)
for_stata.columns = ["X" + str(i) for i in range(k)]
for_stata['ID'] = ID
for_stata['y'] = y
##for_stata.to_stata("resid_test.dta")
print('B', beta, '\n SE: \n', se_beta)
beta = np.squeeze(beta)
t_values = beta / se_beta
print('T values', t_values)
from scipy.stats import t
p_values = 2 * t.cdf(-np.abs(t_values), regpyhdfe.model.df_resid)
# confidence interval size
t_interval = np.asarray(
t.interval(alpha=(1 - alpha), df=regpyhdfe.model.df_resid))
print("t_interval", t_interval)
intervals = np.empty(shape=(beta.shape[0], 2))
# for each variables
for i in range(0, intervals.shape[0]):
intervals[i] = t_interval * se_beta[i] + beta[i]
print('intervals', intervals)
tmp1 = np.linalg.solve(V_beta, np.mat(beta).T)
tmp2 = np.dot(np.mat(beta), tmp1)
fvalue = tmp2[0, 0] / k
import pdb
pdb.set_trace()
print('fvalue', fvalue)
# from statsmodels.stats.stattools import (
# jarque_bera, omni_normtest, durbin_watson)
# jb, jbpv, skew, kurtosis = jarque_bera(self.wresid)
# omni, omnipv = omni_normtest(self.wresid)
# eigvals = self.eigenvals
# condno = self.condition_number
# TODO: Avoid adding attributes in non-__init__
# self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis,
# omni=omni, omnipv=omnipv, condno=condno,
# mineigval=eigvals[-1])
# TODO not used yet
# diagn_left_header = ['Models stats']
# diagn_right_header = ['Residual stats']
# TODO: requiring list/iterable is a bit annoying
# need more control over formatting
# TODO: default do not work if it's not identically spelled
top_left = [
('Dep. Variable:', None),
('Model:', None),
('Method:', ['Least Squares']),
('Date:', None),
('Time:', None),
('No. Observations:', None),
('Df Residuals:', None),
('Df Model:', None),
]
if hasattr(self, 'cov_type'):
top_left.append(('Covariance Type:', [self.cov_type]))
rsquared_type = '' if self.k_constant else ' (uncentered)'
top_right = [
('R-squared' + rsquared_type + ':', ["%#8.3f" % self.rsquared]),
('Adj. R-squared' + rsquared_type + ':',
["%#8.3f" % self.rsquared_adj]),
('F-statistic:', ["%#8.4g" % self.fvalue]),
('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]),
]
# diagn_left = [('Omnibus:', ["%#6.3f" % omni]),
# ('Prob(Omnibus):', ["%#6.3f" % omnipv]),
# ('Skew:', ["%#6.3f" % skew]),
# ('Kurtosis:', ["%#6.3f" % kurtosis])
# ]
#
# diagn_right = [('Durbin-Watson:',
# ["%#8.3f" % durbin_watson(self.wresid)]
# ),
# ('Jarque-Bera (JB):', ["%#8.3f" % jb]),
# ('Prob(JB):', ["%#8.3g" % jbpv]),
# ]
if title is None:
title = self.model.__class__.__name__ + ' ' + "Regression Results"
# create summary table instance
from 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=alpha,
use_t=self.use_t)
# 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 = []
if not self.k_constant:
etext.append("R² is computed without centering (uncentered) since the "
"model does not contain a constant.")
if hasattr(self, 'cov_type'):
etext.append(self.cov_kwds['description'])
if self.model.exog.shape[0] < self.model.exog.shape[1]:
wstr = "The input rank is higher than the number of observations."
etext.append(wstr)
# if eigvals[-1] < 1e-10:
# wstr = "The smallest eigenvalue is %6.3g. This might indicate "
# wstr += "that there are\n"
# wstr += "strong multicollinearity problems or that the design "
# wstr += "matrix is singular."
# wstr = wstr % eigvals[-1]
# etext.append(wstr)
# elif condno > 1000: # TODO: what is recommended?
# wstr = "The condition number is large, %6.3g. This might "
# wstr += "indicate that there are\n"
# wstr += "strong multicollinearity or other numerical "
# wstr += "problems."
# wstr = wstr % condno
# etext.append(wstr)
if etext:
etext = [
"[{0}] {1}".format(i + 1, text) for i, text in enumerate(etext)
]
etext.insert(0, "Notes:")
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
--------
statsmodels.iolib.summary.Summary : class to hold summary
results
"""
# #TODO: import where we need it (for now), add as cached attributes
# from statsmodels.stats.stattools import (jarque_bera,
# omni_normtest, durbin_watson)
# jb, jbpv, skew, kurtosis = jarque_bera(self.wresid)
# omni, omnipv = omni_normtest(self.wresid)
#
eigvals = self.eigenvals
condno = self.condition_number
#
# self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis,
# omni=omni, omnipv=omnipv, condno=condno,
# mineigval=eigvals[0])
top_left = [('Dep. Variable:', None),
('Model:', None),
('Method:', ['Least Squares']),
('Date:', None),
('Time:', None)
]
top_right = [('Pseudo R-squared:', ["%#8.4g" % self.prsquared]),
('Bandwidth:', ["%#8.4g" % self.bandwidth]),
('Sparsity:', ["%#8.4g" % self.sparsity]),
('No. Observations:', None),
('Df Residuals:', None), #[self.df_resid]), #TODO: spelling
('Df Model:', None) #[self.df_model])
]
# diagn_left = [('Omnibus:', ["%#6.3f" % omni]),
# ('Prob(Omnibus):', ["%#6.3f" % omnipv]),
# ('Skew:', ["%#6.3f" % skew]),
# ('Kurtosis:', ["%#6.3f" % kurtosis])
# ]
#
# diagn_right = [('Durbin-Watson:', ["%#8.3f" % durbin_watson(self.wresid)]),
# ('Jarque-Bera (JB):', ["%#8.3f" % jb]),
# ('Prob(JB):', ["%#8.3g" % jbpv]),
# ('Cond. No.', ["%#8.3g" % condno])
# ]
if title is None:
title = self.model.__class__.__name__ + ' ' + "Regression Results"
#create summary table instance
from 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)
# 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 = []
if eigvals[-1] < 1e-10:
wstr = "The smallest eigenvalue is %6.3g. This might indicate "
wstr += "that there are\n"
wstr += "strong multicollinearity problems or that the design "
wstr += "matrix is singular."
wstr = wstr % eigvals[-1]
etext.append(wstr)
elif condno > 1000: #TODO: what is recommended
wstr = "The condition number is large, %6.3g. This might "
wstr += "indicate that there are\n"
wstr += "strong multicollinearity or other numerical "
wstr += "problems."
wstr = wstr % condno
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
def summary(self):
"""
Constructs a summary of the results from a fit model.
Returns
-------
summary : Summary instance
Object that contains tables and facilitated export to text, html or
latex
"""
# Summary layout
# 1. Overall information
# 2. Mean parameters
# 3. Volatility parameters
# 4. Distribution parameters
# 5. Notes
model = self.model
model_name = model.name + " - " + model.volatility.name
# Summary Header
top_left = [
("Dep. Variable:", self._dep_name),
("Mean Model:", model.name),
("Vol Model:", model.volatility.name),
("Distribution:", model.distribution.name),
("Method:", "Maximum Likelihood"),
("", ""),
("Date:", self._datetime.strftime("%a, %b %d %Y")),
("Time:", self._datetime.strftime("%H:%M:%S")),
]
top_right = [
("R-squared:", "%#8.3f" % self.rsquared),
("Adj. R-squared:", "%#8.3f" % self.rsquared_adj),
("Log-Likelihood:", "%#10.6g" % self.loglikelihood),
("AIC:", "%#10.6g" % self.aic),
("BIC:", "%#10.6g" % self.bic),
("No. Observations:", self._nobs),
("Df Residuals:", self.nobs - self.num_params),
("Df Model:", self.num_params),
]
title = model_name + " Model Results"
stubs = []
vals = []
for stub, val in top_left:
stubs.append(stub)
vals.append([val])
table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)
# create summary table instance
smry = Summary()
# Top Table
# Parameter table
fmt = fmt_2cols
fmt["data_fmts"][1] = "%18s"
top_right = [("%-21s" % (" " + k), v) for k, v in top_right]
stubs = []
vals = []
for stub, val in top_right:
stubs.append(stub)
vals.append([val])
table.extend_right(SimpleTable(vals, stubs=stubs))
smry.tables.append(table)
conf_int = np.asarray(self.conf_int())
conf_int_str = []
for c in conf_int:
conf_int_str.append("[" + format_float_fixed(c[0], 7, 3) + "," + format_float_fixed(c[1], 7, 3) + "]")
stubs = self._names
header = ["coef", "std err", "t", "P>|t|", "95.0% Conf. Int."]
vals = (self.params, self.std_err, self.tvalues, self.pvalues, conf_int_str)
formats = [(10, 4), (9, 3), (9, 3), (9, 3), None]
pos = 0
param_table_data = []
for _ in range(len(vals[0])):
row = []
for i, val in enumerate(vals):
if isinstance(val[pos], np.float64):
converted = format_float_fixed(val[pos], *formats[i])
else:
converted = val[pos]
row.append(converted)
pos += 1
param_table_data.append(row)
mc = self.model.num_params
vc = self.model.volatility.num_params
dc = self.model.distribution.num_params
counts = (mc, vc, dc)
titles = ("Mean Model", "Volatility Model", "Distribution")
total = 0
for title, count in zip(titles, counts):
if count == 0:
continue
table_data = param_table_data[total : total + count]
table_stubs = stubs[total : total + count]
total += count
table = SimpleTable(table_data, stubs=table_stubs, txt_fmt=fmt_params, headers=header, title=title)
smry.tables.append(table)
extra_text = ("Covariance estimator: " + self.cov_type,)
smry.add_extra_txt(extra_text)
return smry
