Report if robust covariance matrix in regression
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@ -444,9 +444,17 @@ def regress(
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header_entries = np.vectorize(str.strip)(np.concatenate(np.split(np.array(result.summary().tables[0].data), 2, axis=1)))
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header_dict = {x[0]: x[1] for x in header_entries}
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# Get full name to display
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if model_class is sm.Logit:
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full_name = 'Logistic Regression'
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else:
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full_name = '{} Regression'.format(model_class.__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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result,
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'Logistic Regression' if model_class is sm.Logit else '{} Regression'.format(model_class.__name__), model_class.__name__, header_dict['Method:'],
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full_name, model_class.__name__, header_dict['Method:'],
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dep, result.nobs, result.df_model, datetime.now(),
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terms,
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result.llf, llnull,
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