def print_summary(self, decimals=2, style=None, columns=None, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
style: string
{html, ascii, latex}
columns:
only display a subset of ``summary`` columns. Default all.
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
justify = string_rjustify(18)
headers = []
if self.event_col:
headers.append(("event col", "'%s'" % self.event_col))
if self.weights_col:
headers.append(("weights col", "'%s'" % self.weights_col))
if isinstance(self.penalizer, np.ndarray) or self.penalizer > 0:
headers.append(("penalizer", self.penalizer))
if self.strata:
headers.append(("strata", self.strata))
headers.extend([
("number of subjects", self._n_unique),
("number of periods", self._n_examples),
("number of events", self.event_observed.sum()),
("partial log-likelihood",
"{:.{prec}f}".format(self.log_likelihood_, prec=decimals)),
("time fit was run", self._time_fit_was_called),
])
sr = self.log_likelihood_ratio_test()
footers = []
footers.extend([
("Partial AIC", "{:.{prec}f}".format(self.AIC_partial_,
prec=decimals)),
(
"log-likelihood ratio test",
"{:.{prec}f} on {} df".format(sr.test_statistic,
sr.degrees_freedom,
prec=decimals),
),
("-log2(p) of ll-ratio test",
"{:.{prec}f}".format(-utils.quiet_log2(sr.p_value),
prec=decimals)),
])
p = Printer(self, headers, footers, justify, kwargs, decimals, columns)
p.print(style=style)
def print_summary(self, decimals=2, style=None, columns=None, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
style: string
{html, ascii, latex}
columns:
only display a subset of ``summary`` columns. Default all.
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
justify = string_rjustify(25)
headers = []
headers.append(("duration col", "'%s'" % self.duration_col))
if self.event_col:
headers.append(("event col", "'%s'" % self.event_col))
if self.weights_col:
headers.append(("weights col", "'%s'" % self.weights_col))
if self.coef_penalizer > 0:
headers.append(("coef penalizer", self.coef_penalizer))
if self.smoothing_penalizer > 0:
headers.append(("smoothing penalizer", self.smoothing_penalizer))
headers.extend([
("number of subjects", self._n_examples),
("number of events observed", self.event_observed.sum()),
("time fit was run", self._time_fit_was_called),
])
footers = [("Concordance",
"{:.{prec}f}".format(self.concordance_index_,
prec=decimals))]
p = Printer(self, headers, footers, justify, kwargs, decimals, columns)
p.print(style=style)
def print_summary(self, decimals=2, style=None, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
style: string
{html, ascii, latex}
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
justify = string_justify(18)
headers = []
if self.event_col:
headers.append(("event col", "'%s'" % self.event_col))
if self.weights_col:
headers.append(("weights col", "'%s'" % self.weights_col))
if self.penalizer > 0:
headers.append(("penalizer", self.penalizer))
if self.strata:
headers.append(("strata", self.strata))
headers.extend([
("number of subjects", self._n_unique),
("number of periods", self._n_examples),
("number of events", self.event_observed.sum()),
("partial log-likelihood",
"{:.{prec}f}".format(self.log_likelihood_, prec=decimals)),
("time fit was run", self._time_fit_was_called),
])
p = Printer(headers, self, justify, decimals, kwargs)
p.print(style=style)