Add support for statsmodels PHReg to yli.regress
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@ -129,7 +129,7 @@ class RegressionResult:
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model_class, df, dep, formula, nan_policy, model_kwargs, fit_kwargs,
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raw_result,
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full_name, model_name, fit_method,
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nobs, dof_model, fitted_dt, cov_type,
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nobs, nevents, dof_model, fitted_dt, cov_type,
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terms,
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ll_model, ll_null,
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dof_resid, rsquared, f_statistic,
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@ -166,6 +166,8 @@ class RegressionResult:
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# Basic fitted model information
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#: Number of observations (*int*)
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self.nobs = nobs
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#: Number of events (*int*, time-to-event models only)
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self.nevents = nevents
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#: Degrees of freedom for the model (*int*)
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self.dof_model = dof_model
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#: Date and time of fitting the model (Python *datetime*)
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@ -453,7 +455,7 @@ class RegressionResult:
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self.model_class, self.df, dep, self.formula, self.nan_policy, self.model_kwargs, self.fit_kwargs,
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None,
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self.full_name, self.model_name, self.fit_method,
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self.nobs, self.dof_model, datetime.now(), 'Bootstrap',
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self.nobs, None, self.dof_model, datetime.now(), 'Bootstrap',
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terms,
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self.ll_model, self.ll_null,
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self.dof_resid, self.rsquared, self.f_statistic,
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@ -499,12 +501,14 @@ class RegressionResult:
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right_col = []
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right_col.append(('No. Observations:', format(self.nobs, '.0f')))
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if self.nevents:
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right_col.append(('No. Events:', format(self.nevents, '.0f')))
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right_col.append(('Df. Model:', format(self.dof_model, '.0f')))
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if self.dof_resid:
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right_col.append(('Df. Residuals:', format(self.dof_resid, '.0f')))
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if self.rsquared:
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right_col.append(('<i>R</i><sup>2</sup>:' if html else 'R²:', format(self.rsquared, '.2f')))
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else:
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elif self.ll_null:
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right_col.append(('Pseudo <i>R</i><sup>2</sup>:' if html else 'Pseudo R²:', format(self.pseudo_rsquared, '.2f')))
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if self.f_statistic:
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# Report the F test if available
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@ -516,7 +520,7 @@ class RegressionResult:
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else:
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right_col.append(('F:', format(f_result.statistic, '.2f')))
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right_col.append(('p (F):', fmt_p(f_result.pvalue, PValueStyle.VALUE_ONLY)))
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else:
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elif self.ll_null:
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# Otherwise report likelihood ratio test as overall test
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lrtest_result = self.lrtest_null()
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@ -689,7 +693,7 @@ def regress(
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model_class, df, dep, formula, *,
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nan_policy='warn',
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model_kwargs=None, fit_kwargs=None,
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family=None, exposure=None, # common model_kwargs
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family=None, exposure=None, status=None, # common model_kwargs
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cov_type=None, method=None, maxiter=None, start_params=None, # common fit_kwargs
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bool_baselevels=False, exp=None,
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_dmatrices=None,
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@ -707,6 +711,8 @@ def regress(
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:type formula: str
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:param exposure: Column in *df* for the exposure variable (numeric, some models only)
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:type exposure: str
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:param status: Column in *df* for the status variable (time-to-event models only)
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:type status: str
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:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
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:type nan_policy: str
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:param model_kwargs: Keyword arguments to pass to *model_class* constructor
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@ -780,9 +786,7 @@ def regress(
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# Autodetect whether to exponentiate
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if exp is None:
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if model_class in (sm.Logit, sm.Poisson, PenalisedLogit):
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exp = True
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elif model_class is OrdinalLogit:
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if model_class in (sm.Logit, sm.PHReg, sm.Poisson, OrdinalLogit, PenalisedLogit):
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exp = True
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else:
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exp = False
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@ -790,11 +794,14 @@ def regress(
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df_ref = weakref.ref(df)
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if _dmatrices is None:
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# Check for/clean NaNs
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if exposure is None:
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df = df[[dep] + cols_for_formula(formula, df)]
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else:
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df = df[[dep, exposure] + cols_for_formula(formula, df)]
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# Check for/clean NaNs in input columns
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columns = [dep] + cols_for_formula(formula, df)
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if exposure is not None:
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columns.append(exposure)
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if status is not None:
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columns.append(status)
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df = df[columns]
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df = check_nan(df, nan_policy)
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# Ensure numeric type for dependent variable
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@ -808,17 +815,22 @@ def regress(
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else:
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dmatrices = _dmatrices
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if model_class is OrdinalLogit:
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if model_class in (sm.PHReg, OrdinalLogit):
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# Drop explicit intercept term
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# FIXME: Check before dropping
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dmatrices = (dmatrices[0], dmatrices[1].iloc[:,1:])
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# Add exposure to model
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if exposure is not None:
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if df[exposure].dtype == '<m8[ns]':
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model_kwargs['exposure'] = df[exposure].dt.total_seconds()
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else:
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model_kwargs['exposure'] = df[exposure]
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# Add status to model
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if status is not None:
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model_kwargs['status'] = df[status]
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# Fit model
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model = model_class(endog=dmatrices[0], exog=dmatrices[1], **model_kwargs)
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model.formula = dep + ' ~ ' + formula
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@ -845,11 +857,19 @@ def regress(
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# Join term names manually because statsmodels wrapper is very slow
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#confint = result.conf_int(config.alpha)
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confint = {k: v for k, v in zip(model.exog_names, result._results.conf_int(config.alpha))}
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pvalues = {k: v for k, v in zip(model.exog_names, result._results.pvalues)}
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if hasattr(result, '_results'):
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confint = {k: v for k, v in zip(model.exog_names, result._results.conf_int(config.alpha))}
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pvalues = {k: v for k, v in zip(model.exog_names, result._results.pvalues)}
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params = result._results.params
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else:
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# e.g. PHReg
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confint = {k: v for k, v in zip(model.exog_names, result.conf_int(config.alpha))}
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pvalues = {k: v for k, v in zip(model.exog_names, result.pvalues)}
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params = result.params
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#for raw_name, raw_beta in result.params.items():
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for raw_name, raw_beta in zip(model.exog_names, result._results.params):
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for raw_name, raw_beta in zip(model.exog_names, params):
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beta = Estimate(raw_beta, confint[raw_name][0], confint[raw_name][1])
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# Rename terms
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@ -896,6 +916,8 @@ def regress(
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ll_null = result.ll_null
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elif hasattr(result, 'llnull'):
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ll_null = result.llnull
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elif model_class is sm.PHReg:
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ll_null = None
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else:
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# Construct null (intercept-only) model
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#result_null = model_class.from_formula(formula=dep + ' ~ 1', data=df).fit()
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@ -908,6 +930,8 @@ def regress(
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if model_class is sm.OLS:
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method_name = 'Least Squares'
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elif model_class is sm.PHReg:
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method_name = 'MPLE'
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else:
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# Parse raw regression results to get fit method
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# Avoid this in general as it can be expensive to summarise all the post hoc tests, etc.
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@ -915,21 +939,26 @@ def regress(
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header_dict = {x[0]: x[1] for x in header_entries}
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method_name = header_dict['Method:']
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# Get full name to display
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# Get names to display
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if model_class is sm.PHReg:
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short_name = 'Cox'
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else:
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short_name = model_class.__name__
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if model_class is sm.Logit:
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full_name = 'Logistic Regression'
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elif model_class is OrdinalLogit:
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full_name = 'Ordinal Logistic Regression'
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else:
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full_name = '{} Regression'.format(model_class.__name__)
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full_name = '{} Regression'.format(short_name)
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if fit_kwargs.get('cov_type', 'nonrobust') != 'nonrobust':
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full_name = 'Robust {}'.format(full_name)
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return RegressionResult(
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model_class, df_ref, dep, formula, nan_policy, model_kwargs, fit_kwargs,
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result,
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full_name, model_class.__name__, method_name,
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result.nobs, result.df_model, datetime.now(), getattr(result, 'cov_type', 'nonrobust'),
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full_name, short_name, method_name,
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getattr(result, 'nobs', len(df)), df[status].sum() if model_class is sm.PHReg else None, result.df_model, datetime.now(), getattr(result, 'cov_type', 'nonrobust'),
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terms,
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result.llf, ll_null,
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getattr(result, 'df_resid', None), getattr(result, 'rsquared', None), getattr(result, 'fvalue', None),
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@ -1048,7 +1077,7 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
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None, None, None, None, None, None, None, # Set in regress()
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model,
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'Penalised Logistic Regression', 'Logit', 'Penalised ML',
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model['n'][0], model['df'][0], datetime.now(), 'nonrobust',
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model['n'][0], None, model['df'][0], datetime.now(), 'nonrobust',
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terms,
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model['loglik'][0], model['loglik'][1],
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None, None, None,
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