Add unit test for RegressionResult.brant

tests/test_ordinallogit.py

@@ -62,3 +62,38 @@ somewhat likely/very likely    4.30  (2.72 - 5.88)   <0.001*
 ------------------------------------------------------------'''.format(result.fitted_dt)
 	
 	assert result.summary() == expected_summary
+
+def test_brant_ucla():
+	"""Compare RegressionResult.brant with R brant library for UCLA example at https://stats.oarc.ucla.edu/r/dae/ordinal-logistic-regression/"""
+	
+	df = pd.read_stata('tests/data/ucla_ologit.dta')
+	result = yli.regress(yli.OrdinalLogit, df, 'apply', 'pared + public + gpa', exp=False)
+	brant_result = result.brant()
+	
+	# df <- read.dta("https://stats.idre.ucla.edu/stat/data/ologit.dta")
+	# m <- polr(apply ~ pared + public + gpa, data=df, Hess=TRUE)
+	# brant(m)
+	
+	assert brant_result.tests['Omnibus'].statistic == approx(4.34, abs=0.01)
+	assert brant_result.tests['Omnibus'].dof == 3
+	assert brant_result.tests['Omnibus'].pvalue == approx(0.23, abs=0.01)
+	
+	assert brant_result.tests['pared'].statistic == approx(0.13, abs=0.01)
+	assert brant_result.tests['pared'].dof == 1
+	assert brant_result.tests['pared'].pvalue == approx(0.72, abs=0.01)
+	
+	assert brant_result.tests['public'].statistic == approx(3.44, abs=0.01)
+	assert brant_result.tests['public'].dof == 1
+	assert brant_result.tests['public'].pvalue == approx(0.06, abs=0.01)
+	
+	assert brant_result.tests['gpa'].statistic == approx(0.18, abs=0.01)
+	assert brant_result.tests['gpa'].dof == 1
+	assert brant_result.tests['gpa'].pvalue == approx(0.67, abs=0.01)
+	
+	expected_summary = '''          χ²  df     p   
+Omnibus  4.34  3   0.23  
+pared    0.13  1   0.72  
+public   3.44  1   0.06  
+gpa      0.18  1   0.67  '''
+	
+	assert brant_result.summary() == expected_summary

yli/regress.py

@@ -1097,5 +1097,9 @@ class BrantResult:
 		:rtype: str
 		"""
 		
-		# FIXME
-		return 'FIXME'
+		table = pd.DataFrame([
+			['{:.2f}'.format(test.statistic), '{:.0f}'.format(test.dof), fmt_p(test.pvalue, PValueStyle.TABULAR)]
+			for test in self.tests.values()
+		], index=self.tests.keys(), columns=['χ² ', 'df', 'p   '])
+		
+		return str(table)