def summary(self):
""":obj:`statsmodels.iolib.summary.Summary` : Summary table of model estimation results
Supports export to csv, html and latex using the methods ``summary.as_csv()``,
``summary.as_html()`` and ``summary.as_latex()``.
"""
stubs_lookup = {
'rsquared': 'R-squared',
'partial.rsquared': 'Partial R-squared',
'shea.rsquared': 'Shea\'s R-squared',
'f.stat': 'Partial F-statistic',
'f.pval': 'P-value (Partial F-stat)',
'f.dist': 'Partial F-stat Distn'
}
smry = Summary()
diagnostics = self.diagnostics
vals = []
for c in diagnostics:
if c != 'f.dist':
vals.append([_str(v) for v in diagnostics[c]])
else:
vals.append([v for v in diagnostics[c]])
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)
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):
"""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):
"""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):
""":obj:`statsmodels.iolib.summary.Summary` : Summary table of model estimation results
Supports export to csv, html and latex using the methods ``summary.as_csv()``,
``summary.as_html()`` and ``summary.as_latex()``.
"""
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 = OrderedDict()
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
precision = getattr(self, self._precision)
params_fmt = []
params_stub = []
for i in range(len(params)):
params_fmt.append([_str(v) for v in params.values[i]])
precision_fmt = []
for v in precision.values[i]:
v_str = _str(v)
v_str = '({0})'.format(v_str) if v_str.strip() else v_str
precision_fmt.append(v_str)
params_fmt.append(precision_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(len, 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)
prec_type = self._PRECISION_TYPES[self._precision]
smry.add_extra_txt(['{0} reported in parentheses'.format(prec_type)])
return smry