Add documentation for RegressionResult.brant

docs/regress.rst

@@ -6,17 +6,20 @@ Functions
 
 .. autofunction:: yli.logit_then_regress
 
+.. autoclass:: yli.OrdinalLogit
+
 .. autoclass:: yli.PenalisedLogit
 
 .. autofunction:: yli.regress
 
-.. autofunction:: yli.regress_bootstrap
-
 .. autofunction:: yli.vif
 
 Result classes
 --------------
 
+.. autoclass:: yli.regress.BrantResult
+	:members:
+
 .. autoclass:: yli.regress.CategoricalTerm
 	:members:
 

yli/regress.py

@@ -256,7 +256,33 @@ class RegressionResult:
 			return bf01.invert()
 	
 	def brant(self):
-		# TODO: Documentation
+		"""
+		Perform the Brant test for the parallel regression assumption in ordinal regression
+		
+		Applicable when the model was fitted using :class:`OrdinalLogit`.
+		
+		:rtype: :class:`BrantResult`
+		
+		**Example:**
+		
+		.. code-block::
+			
+			df = pd.DataFrame(...)
+			model = yli.regress(yli.OrdinalLogit, df, 'apply', 'pared + public + gpa', exp=False)
+			model.brant()
+		
+		.. code-block:: text
+			
+			          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  
+		
+		The output shows the result of the Brant test. For example, for the omnibus test of the parallel regression assumption across all independent variables, the *χ*:sup:`2` statistic is 4.34, the *χ*:sup:`2` distribution has 3 degrees of freedom, and the test is not significant, with *p* value 0.23.
+		
+		**Reference:** Brant R. Assessing proportionality in the proportional odds model for ordinal logistic regression. *Biometrics*. 1990;46(4):1171–8. `doi:10.2307/2532457 <https://doi.org/10.2307/2532457>`_
+		"""
 		
 		df = self.df()
 		if df is None:
@@ -1070,7 +1096,11 @@ def _wald_test(param_names, params, formula, vcov, df):
 	return lmr.wald_test(formula, cov_p=vcov, use_f=False, scalar=True)
 
 class BrantResult:
-	# TODO: Documentation
+	"""
+	Result of a Brant test for ordinal regression
+	
+	See :meth:`RegressionResult.brant`.
+	"""
 	
 	def __init__(self, tests):
 		#: Results for Brant test on each coefficient (*Dict[str, ChiSquaredResult]*)