Observe config.alpha for significance tests and regression

yli/distributions.py

@@ -20,6 +20,8 @@ from scipy import integrate, optimize, stats
 
 import collections
 
+from .config import config
+
 class betarat_gen(stats.rv_continuous):
 	"""
 	Ratio of 2 independent beta-distributed variables
@@ -283,10 +285,17 @@ transformed_dist = transformed_gen(name='transformed')
 
 ConfidenceInterval = collections.namedtuple('ConfidenceInterval', ['lower', 'upper'])
 
-def hdi(distribution, level=0.95):
+def hdi(distribution, level=None):
-	"""Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)"""
+	"""
+	Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)
 	
-	# For a given lower limit, we can compute the corresponding 95% interval
+	level: Coverage/confidence probability, default (None) is 1 - config.alpha
+	"""
+	
+	if level is None:
+		level = 1 - config.alpha
+	
+	# For a given lower limit, we can compute the corresponding level*100% interval
 	def interval_width(lower):
 		upper = distribution.ppf(distribution.cdf(lower) + level)
 		return upper - lower

yli/regress.py

@@ -26,6 +26,7 @@ from datetime import datetime
 import itertools
 
 from .bayes_factors import BayesFactor, bayesfactor_afbf
+from .config import config
 from .sig_tests import FTestResult
 from .utils import Estimate, check_nan, cols_for_formula, fmt_p, formula_factor_ref_category, parse_patsy_term
 
@@ -220,7 +221,7 @@ class RegressionResult:
 		out += '</table>'
 		
 		# Render coefficients table
-		out += '<table><tr><th></th><th style="text-align:center">{}</th><th colspan="3" style="text-align:center">(95% CI)</th><th style="text-align:center"><i>p</i></th></tr>'.format('exp(<i>β</i>)' if self.exp else '<i>β</i>')
+		out += '<table><tr><th></th><th style="text-align:center">{}</th><th colspan="3" style="text-align:center">({:g}% CI)</th><th style="text-align:center"><i>p</i></th></tr>'.format('exp(<i>β</i>)' if self.exp else '<i>β</i>', (1-config.alpha)*100)
 		
 		for term_name, term in self.terms.items():
 			if isinstance(term, SingleTerm):
@@ -306,7 +307,7 @@ class RegressionResult:
 				raise Exception('Attempt to render unknown term type')
 		
 		table2 = SimpleTable(data=table_data, headers=['', 'exp(β)' if self.exp else 'β', '', '\ue000', '', ' p'])  # U+E000 is in Private Use Area, mark middle of CI column
-		table2_text = table2.as_text().replace('    \ue000   ', '(95% CI)')  # Render heading in the right spot
+		table2_text = table2.as_text().replace('    \ue000   ', '({:g}% CI)'.format((1-config.alpha)*100))  # Render heading in the right spot
 		table2_lines = table2_text.splitlines(keepends=False)
 		
 		# Render divider line between 2 tables
@@ -382,7 +383,7 @@ def regress(
 	# Process terms
 	terms = {}
 	
-	confint = result.conf_int()
+	confint = result.conf_int(config.alpha)
 	
 	for raw_name, raw_beta in result.params.items():
 		beta = Estimate(raw_beta, confint[0][raw_name], confint[1][raw_name])
@@ -466,9 +467,10 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
 				
 				# Transfer other parameters to R
 				lc['formula_'] = self.formula
+				lc['alpha'] = config.alpha
 				
 				# Fit the model
-				model = ro.r('logistf(formula_, data=df)')
+				model = ro.r('logistf(formula_, data=df, alpha=alpha)')
 				
 				# TODO: Handle categorical terms?
 				terms = {t: SingleTerm(t, Estimate(b, ci0, ci1), p) for t, b, ci0, ci1, p in zip(model['terms'], model['coefficients'], model['ci.lower'], model['ci.upper'], model['prob'])}

yli/sig_tests.py

@@ -22,6 +22,7 @@ import statsmodels.api as sm
 import functools
 import warnings
 
+from .config import config
 from .utils import Estimate, as_2groups, check_nan, fmt_p
 
 # ----------------
@@ -42,10 +43,10 @@ class TTestResult:
 		self.delta_direction = delta_direction
 	
 	def _repr_html_(self):
-		return '<i>t</i>({:.0f}) = {:.2f}; <i>p</i> {}<br><i>δ</i> (95% CI) = {}, {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=True), self.delta.summary(), self.delta_direction)
+		return '<i>t</i>({:.0f}) = {:.2f}; <i>p</i> {}<br><i>δ</i> ({:g}% CI) = {}, {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=True), (1-config.alpha)*100, self.delta.summary(), self.delta_direction)
 	
 	def summary(self):
-		return 't({:.0f}) = {:.2f}; p {}\nδ (95% CI) = {}, {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=False), self.delta.summary(), self.delta_direction)
+		return 't({:.0f}) = {:.2f}; p {}\nδ ({:g}% CI) = {}, {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=False), (1-config.alpha)*100, self.delta.summary(), self.delta_direction)
 
 def ttest_ind(df, dep, ind, *, nan_policy='warn'):
 	"""Perform an independent-sample Student's t test"""
@@ -65,7 +66,7 @@ def ttest_ind(df, dep, ind, *, nan_policy='warn'):
 	statistic, pvalue, dof = cm.ttest_ind()
 	
 	delta = d1.mean - d2.mean
-	ci0, ci1 = cm.tconfint_diff()
+	ci0, ci1 = cm.tconfint_diff(config.alpha)
 	
 	# t test is symmetric so take absolute values
 	return TTestResult(
@@ -209,16 +210,16 @@ class PearsonChiSquaredResult:
 	
 	def _repr_html_(self):
 		if self.oddsratio is not None:
-			return '{}<br><i>χ</i><sup>2</sup>({}) = {:.2f}; <i>p</i> {}<br>OR (95% CI) = {}<br>RR (95% CI) = {}'.format(
+			return '{0}<br><i>χ</i><sup>2</sup>({1}) = {2:.2f}; <i>p</i> {3}<br>OR ({4:g}% CI) = {5}<br>RR ({4:g}% CI) = {6}'.format(
-				self.ct._repr_html_(), self.dof, self.statistic, fmt_p(self.pvalue, html=True), self.oddsratio.summary(), self.riskratio.summary())
+				self.ct._repr_html_(), self.dof, self.statistic, fmt_p(self.pvalue, html=True), (1-config.alpha)*100, self.oddsratio.summary(), self.riskratio.summary())
 		else:
 			return '{}<br><i>χ</i><sup>2</sup>({}) = {:.2f}; <i>p</i> {}'.format(
 				self.ct._repr_html_(), self.dof, self.statistic, fmt_p(self.pvalue, html=True))
 	
 	def summary(self):
 		if self.oddsratio is not None:
-			return '{}\nχ²({}) = {:.2f}; p {}\nOR (95% CI) = {}\nRR (95% CI) = {}'.format(
+			return '{0}\nχ²({1}) = {2:.2f}; p {3}\nOR ({4:g}% CI) = {5}\nRR ({4:g}% CI) = {6}'.format(
-				self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False), self.oddsratio.summary(), self.riskratio.summary())
+				self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False), (1-config.alpha)*100, self.oddsratio.summary(), self.riskratio.summary())
 		else:
 			return '{}\nχ²({}) = {:.2f}; p {}'.format(
 				self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False))
@@ -247,8 +248,8 @@ def chi2(df, dep, ind, *, nan_policy='warn'):
 		
 		smct = sm.stats.Table2x2(np.flip(ct.to_numpy()), shift_zeros=False)
 		result = smct.test_nominal_association()
-		ORci = smct.oddsratio_confint()
+		ORci = smct.oddsratio_confint(config.alpha)
-		RRci = smct.riskratio_confint()
+		RRci = smct.riskratio_confint(config.alpha)
 		
 		return PearsonChiSquaredResult(
 			ct=ct, statistic=result.statistic, dof=result.df, pvalue=result.pvalue,
@@ -272,10 +273,10 @@ class PearsonRResult:
 		self.pvalue = pvalue
 	
 	def _repr_html_(self):
-		return '<i>r</i> (95% CI) = {}; <i>p</i> {}'.format(self.statistic.summary(), fmt_p(self.pvalue, html=True))
+		return '<i>r</i> ({:g}% CI) = {}; <i>p</i> {}'.format((1-config.alpha)*100, self.statistic.summary(), fmt_p(self.pvalue, html=True))
 	
 	def summary(self):
-		return 'r (95% CI) = {}; p {}'.format(self.statistic.summary(), fmt_p(self.pvalue, html=False))
+		return 'r ({:g}% CI) = {}; p {}'.format((1-config.alpha)*100, self.statistic.summary(), fmt_p(self.pvalue, html=False))
 
 def pearsonr(df, dep, ind, *, nan_policy='warn'):
 	"""Compute the Pearson correlation coefficient (Pearson's r)"""