From 7a93355dab5a6b6f8d392feed5645d6804a65d13 Mon Sep 17 00:00:00 2001
From: RunasSudo <runassudo@yingtongli.me>
Date: Mon, 17 Oct 2022 20:34:58 +1100
Subject: [PATCH] Show covariance matrix type in regression results

---
 yli/regress.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/yli/regress.py b/yli/regress.py
index ad2d4e6..3b7db67 100644
--- a/yli/regress.py
+++ b/yli/regress.py
@@ -86,7 +86,7 @@ class RegressionResult:
 	def __init__(self,
 		raw_result,
 		full_name, model_name, fit_method,
-		dep, nobs, dof_model, fitted_dt,
+		dep, nobs, dof_model, fitted_dt, cov_type,
 		terms,
 		llf, llnull,
 		dof_resid, rsquared, f_statistic,
@@ -105,6 +105,7 @@ class RegressionResult:
 		self.nobs = nobs
 		self.dof_model = dof_model
 		self.fitted_dt = fitted_dt
+		self.cov_type = cov_type
 		
 		# Regression coefficients/p values
 		self.terms = terms
@@ -175,11 +176,12 @@ class RegressionResult:
 		left_col.append(('Method:', self.fit_method))
 		left_col.append(('Date:', self.fitted_dt.strftime('%Y-%m-%d')))
 		left_col.append(('Time:', self.fitted_dt.strftime('%H:%M:%S')))
-		left_col.append(('No. Observations:', format(self.nobs, '.0f')))
+		left_col.append(('Std. Errors:', 'Non-Robust' if self.cov_type == 'nonrobust' else self.cov_type.upper()))
 		
 		# Right column
 		right_col = []
 		
+		right_col.append(('No. Observations:', format(self.nobs, '.0f')))
 		right_col.append(('Df. Model:', format(self.dof_model, '.0f')))
 		if self.dof_resid:
 			right_col.append(('Df. Residuals:', format(self.dof_resid, '.0f')))
@@ -463,7 +465,7 @@ def regress(
 	return RegressionResult(
 		result,
 		full_name, model_class.__name__, header_dict['Method:'],
-		dep, result.nobs, result.df_model, datetime.now(),
+		dep, result.nobs, result.df_model, datetime.now(), result.cov_type,
 		terms,
 		result.llf, llnull,
 		getattr(result, 'df_resid', None), getattr(result, 'rsquared', None), getattr(result, 'fvalue', None),
@@ -500,7 +502,7 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
 	NB: This class expects to be used in the context of yli.regress()
 	"""
 	
-	def fit(self):
+	def fit(self, disp=False):
 		import rpy2.robjects as ro
 		import rpy2.robjects.packages
 		import rpy2.robjects.pandas2ri
@@ -532,7 +534,7 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
 				return RegressionResult(
 					model,
 					'Penalised Logistic Regression', 'Logit', 'Penalised ML',
-					self.endog_names, model['n'][0], model['df'][0], datetime.now(),
+					self.endog_names, model['n'][0], model['df'][0], datetime.now(), 'nonrobust',
 					terms,
 					model['loglik'][0], model['loglik'][1],
 					None, None, None,