Add auto_univariable

docs/sig_tests.rst

@@ -6,6 +6,8 @@ Functions
 
 .. autofunction:: yli.anova_oneway
 
+.. autofunction:: yli.auto_univariable
+
 .. autofunction:: yli.chi2
 
 .. autofunction:: yli.mannwhitney
@@ -17,6 +19,9 @@ Functions
 Result classes
 --------------
 
+.. autoclass:: yli.sig_tests.AutoBinaryResult
+	:members:
+
 .. autoclass:: yli.sig_tests.BrunnerMunzelResult
 	:members:
 

yli/__init__.py

@@ -19,7 +19,7 @@ from .config import config
 from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
 from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
 from .regress import PenalisedLogit, logit_then_regress, regress, vif
-from .sig_tests import anova_oneway, chi2, mannwhitney, pearsonr, ttest_ind
+from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, ttest_ind
 
 def reload_me():
 	import importlib

yli/sig_tests.py

@@ -98,6 +98,18 @@ class TTestResult:
 		"""
 		
 		return '{}\n\nt({:.0f}) = {:.2f}; p {}\nΔμ ({:g}% CI) = {}, {}'.format(self._comparison_table(False), self.dof, self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION), (1-config.alpha)*100, self.delta.summary(), self.delta_direction)
+	
+	def summary_short(self, html):
+		"""
+		Return a stringified summary of the *t* test (*t* statistic only)
+		
+		:rtype: str
+		"""
+		
+		if html:
+			return '<i>t</i>({:.0f}) = {:.2f}'.format(self.dof, self.statistic)
+		else:
+			return 't({:.0f}) = {:.2f}'.format(self.dof, self.statistic)
 
 def ttest_ind(df, dep, ind, *, nan_policy='warn'):
 	"""
@@ -336,6 +348,18 @@ class MannWhitneyResult:
 			return line1 + '\n' + self.brunnermunzel.summary()
 		else:
 			return line1
+	
+	def summary_short(self, html):
+		"""
+		Return a stringified summary of the Mann–Whitney test (*U* statistic only)
+		
+		:rtype: str
+		"""
+		
+		if html:
+			return '<i>U</i> = {:.1f}'.format(self.statistic)
+		else:
+			return 'U = {:.1f}'.format(self.statistic)
 
 class BrunnerMunzelResult:
 	"""
@@ -422,7 +446,7 @@ def mannwhitney(df, dep, ind, *, nan_policy='warn', brunnermunzel=True, use_cont
 	# Check for/clean NaNs
 	df = check_nan(df[[ind, dep]], nan_policy)
 	
-	# Convert pandas nullable types for independent variables as this breaks statsmodels
+	# Convert pandas nullable types for independent variables as this breaks mannwhitneyu
 	df = convert_pandas_nullable(df)
 	
 	# Ensure 2 groups for ind
@@ -506,6 +530,18 @@ class PearsonChiSquaredResult:
 		else:
 			return '{}\n\nχ²({}) = {:.2f}; p {}'.format(
 				self.ct, self.dof, self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION))
+	
+	def summary_short(self, html):
+		"""
+		Return a stringified summary of the *χ*:sup:`2` test (*χ*:sup:`2` statistic only)
+		
+		:rtype: str
+		"""
+		
+		if html:
+			return '<i>χ</i><sup>2</sup>({}) = {:.2f}'.format(self.dof, self.statistic)
+		else:
+			return 'χ²({}) = {:.2f}'.format(self.dof, self.statistic)
 
 def chi2(df, dep, ind, *, nan_policy='warn'):
 	"""
@@ -662,3 +698,144 @@ def pearsonr(df, dep, ind, *, nan_policy='warn'):
 	ci = result.confidence_interval()
 	
 	return PearsonRResult(statistic=Estimate(result.statistic, ci.low, ci.high), pvalue=result.pvalue)
+
+# ----------------------------
+# Automatic selection of tests
+
+class AutoBinaryResult:
+	"""
+	Result of automatically computed univariable tests of association for a dichotomous dependent variable
+	
+	See :func:`yli.auto_univariable`.
+	
+	Results data stored within instances of this class is not intended to be directly accessed.
+	"""
+	
+	def __init__(self, *, dep, group1, group2, result_data, result_labels):
+		#: Name of the dependent variable (*str*)
+		self.dep = dep
+		
+		#: Name of the first group (*str*)
+		self.group1 = group1
+		#: Name of the second group (*str*)
+		self.group2 = group2
+		
+		# List of tuples (first group summary, second group summary, test result)
+		self._result_data = result_data
+		# List of row labels for the independente variables
+		self._result_labels = result_labels
+	
+	def __repr__(self):
+		if config.repr_is_summary:
+			return self.summary()
+		return super().__repr__()
+	
+	def _repr_html_(self):
+		result = '<table><thead><tr><th>{}</th><th>{}</th><th>{}</th><th></th><th style="text-align:center"><i>p</i></th></tr></thead><tbody>'.format(self.dep, self.group1, self.group2)
+		
+		for data, label in zip(self._result_data, self._result_labels):
+			result += '<tr><th>{}</th><td>{}</td><td>{}</td><td style="text-align:left">{}</td><td style="text-align:left">{}</td></tr>'.format(label[1], data[0], data[1], data[2].summary_short(True), fmt_p(data[2].pvalue, PValueStyle.TABULAR | PValueStyle.HTML))
+		
+		result += '</tbody></table>'
+		return result
+	
+	def summary(self):
+		"""
+		Return a stringified summary of the tests of association
+		
+		:rtype: str
+		"""
+		
+		# Format data for output
+		result_data_fmt = [
+			[r[0], r[1], r[2].summary_short(False), fmt_p(r[2].pvalue, PValueStyle.TABULAR)]
+			for r in self._result_data
+		]
+		result_labels_fmt = [r[0] for r in self._result_labels]
+		
+		table = pd.DataFrame(result_data_fmt, index=result_labels_fmt, columns=pd.Index([self.group1, self.group2, '', 'p'], name=self.dep))
+		return str(table)
+
+def auto_univariable(df, dep, inds, *, ordinal=[], nan_policy='warn'):
+	"""
+	Automatically compute univariable tests of association for a dichotomous dependent variable
+	
+	The tests performed are:
+	
+	* For a dichotomous independent variable – :func:`yli.chi2`
+	* For a continuous independent variable – :func:`yli.ttest_ind`
+	* For an ordinal independent variable – :func:`yli.mannwhitney`
+	
+	:param df: Data to perform the test on
+	:type df: DataFrame
+	:param dep: Column in *df* for the dependent variable (dichotomous)
+	:type dep: str
+	:param inds: Columns in *df* for the independent variables
+	:type inds: List[str]
+	:param ordinal: Columns in *df* to treat as ordinal rather than continuous
+	:type ordinal: List[str]
+	:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
+	:type nan_policy: str
+	
+	:rtype: :class:`yli.sig_tests.AutoBinaryResult`
+	"""
+	
+	# Check for/clean NaNs
+	# Following this, we pass nan_policy='raise' to assert no NaNs remaining
+	df = check_nan(df[inds + [dep]], nan_policy)
+	
+	# Ensure 2 groups for dep
+	# TODO: Work for non-binary dependent variables?
+	group1, data1, group2, data2 = as_2groups(df, inds, dep)
+	
+	result_data = []
+	result_labels = []
+	
+	for ind in inds:
+		if df[ind].dtype in ('bool', 'category', 'object'):
+			# Pearson chi-squared test
+			result = chi2(df, dep, ind, nan_policy='raise')
+			
+			values = sorted(df[ind].unique())
+			
+			# Value counts
+			result_labels.append((
+				'{}, {}'.format(ind, ':'.join(str(v) for v in values)),
+				'{}, {}'.format(ind, ':'.join(str(v) for v in values)),
+			))
+			result_data.append((
+				':'.join(str((data1[ind] == v).sum()) for v in values),
+				':'.join(str((data2[ind] == v).sum()) for v in values),
+				result
+			))
+		elif df[ind].dtype in ('float64', 'int64', 'Float64', 'Int64'):
+			if ind in ordinal:
+				# Mann-Whitney test
+				result = mannwhitney(df, ind, dep, nan_policy='raise')
+				
+				result_labels.append((
+					'{}, median (IQR)'.format(ind),
+					'{}, median (IQR)'.format(ind),
+				))
+				result_data.append((
+					'{:.2f} ({})'.format(result.med1, result.iqr1.summary()),
+					'{:.2f} ({})'.format(result.med2, result.iqr2.summary()),
+					result
+				))
+			else:
+				# t test
+				result = ttest_ind(df, ind, dep, nan_policy='raise')
+				
+				result_labels.append((
+					'{}, μ (SD)'.format(ind),
+					'{}, <i>μ</i> (SD)'.format(ind),
+				))
+				result_data.append((
+					'{:.2f} ({:.2f})'.format(result.mu1, result.sd1),
+					'{:.2f} ({:.2f})'.format(result.mu2, result.sd2),
+					result
+				))
+		else:
+			raise Exception('Unsupported independent dtype for auto_univariable, {}'.format(df[ind].dtype))
+	
+	return AutoBinaryResult(dep=dep, group1=group1, group2=group2, result_data=result_data, result_labels=result_labels)