Update documentation

docs/sig_tests.rst

@@ -14,6 +14,8 @@ Functions
 
 .. autofunction:: yli.pearsonr
 
+.. autofunction:: yli.spearman
+
 .. autofunction:: yli.ttest_ind
 
 Result classes
@@ -38,5 +40,8 @@ Result classes
 .. autoclass:: yli.sig_tests.PearsonRResult
 	:members:
 
+.. autoclass:: yli.sig_tests.SpearmanResult
+	:members:
+
 .. autoclass:: yli.sig_tests.TTestResult
 	:members:

yli/sig_tests.py

@@ -606,7 +606,7 @@ def chi2(df, dep, ind, *, nan_policy='warn'):
 	
 	The odds of *Stress* in the *Response* = *True* group are 1.33 times that in the *Response* = *False* group, with 95% confidence interval 1.11–1.60.
 	
-	The risk of *Stress* in the *Response* = *True* group is 1.11 that in the *Response* = *False* group, with 95% confidence interval 1.03–1.18.
+	The risk of *Stress* in the *Response* = *True* group is 1.11 times that in the *Response* = *False* group, with 95% confidence interval 1.03–1.18.
 	"""
 	
 	# Check for/clean NaNs
@@ -649,13 +649,13 @@ def chi2(df, dep, ind, *, nan_policy='warn'):
 
 class PearsonRResult:
 	"""
-	Result of Pearson correlation
+	Result of Pearson product-moment correlation
 	
 	See :func:`yli.pearsonr`.
 	"""
 	
 	def __init__(self, statistic, pvalue):
-		#: Pearson *r* correlation statistic (*float*)
+		#: Pearson *r* correlation statistic (:class:`yli.utils.Estimate`)
 		self.statistic = statistic
 		#: *p* value for the *r* statistic (*float*)
 		self.pvalue = pvalue
@@ -679,7 +679,7 @@ class PearsonRResult:
 
 def pearsonr(df, dep, ind, *, nan_policy='warn'):
 	"""
-	Compute the Pearson correlation coefficient (Pearson's *r*)
+	Compute the Pearson product-moment correlation coefficient (Pearson's *r*)
 	
 	:param df: Data to perform the test on
 	:type df: DataFrame
@@ -722,8 +722,16 @@ def pearsonr(df, dep, ind, *, nan_policy='warn'):
 # Spearman correlation
 
 class SpearmanResult:
+	"""
+	Result of Spearman rank correlation
+	
+	See :func:`yli.spearman`.
+	"""
+	
 	def __init__(self, statistic, pvalue):
+		#: Spearman *ρ* correlation statistic (:class:`yli.utils.Estimate`)
 		self.statistic = statistic
+		#: *p* value for the *ρ* statistic (*float*)
 		self.pvalue = pvalue
 	
 	def __repr__(self):
@@ -744,6 +752,39 @@ class SpearmanResult:
 		return 'ρ ({:g}% CI) = {}; p {}'.format((1-config.alpha)*100, self.statistic.summary(), fmt_p(self.pvalue, PValueStyle.RELATION))
 
 def spearman(df, dep, ind, *, nan_policy='warn'):
+	"""
+	Compute the Spearman rank correlation coefficient (Spearman's *ρ*)
+	
+	The confidence interval for *ρ* is computed analogously to SciPy's *pearsonr*, using the Fisher transformation and normal approximation, without adjustment to variance.
+	
+	:param df: Data to perform the test on
+	:type df: DataFrame
+	:param dep: Column in *df* for the dependent variable (numerical)
+	:type dep: str
+	:param ind: Column in *df* for the independent variable (numerical)
+	:type ind: str
+	:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
+	:type nan_policy: str
+	
+	:rtype: :class:`yli.sig_tests.SpearmanResult`
+	
+	**Example:**
+	
+	.. code-block::
+		
+		df = pd.DataFrame({
+			'Profit': [2.5, 6.2, 3.1, ...],
+			'Quality': [50, 57, 61, ...]
+		})
+		yli.spearman(df, 'Profit', 'Quality')
+	
+	.. code-block:: text
+		
+		ρ (95% CI) = 0.87 (0.60–0.96); p < 0.001*
+	
+	The output states that the value of the Spearman correlation coefficient is 0.87, with 95% confidence interval 0.60–0.96, and the test is significant with *p* value < 0.001.
+	"""
+	
 	# Check for/clean NaNs
 	df = check_nan(df[[ind, dep]], nan_policy)