Observe config.alpha for significance tests and regression
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@ -20,6 +20,8 @@ from scipy import integrate, optimize, stats
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import collections
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from .config import config
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class betarat_gen(stats.rv_continuous):
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"""
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Ratio of 2 independent beta-distributed variables
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@ -283,10 +285,17 @@ transformed_dist = transformed_gen(name='transformed')
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ConfidenceInterval = collections.namedtuple('ConfidenceInterval', ['lower', 'upper'])
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def hdi(distribution, level=0.95):
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"""Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)"""
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def hdi(distribution, level=None):
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"""
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Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)
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# For a given lower limit, we can compute the corresponding 95% interval
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level: Coverage/confidence probability, default (None) is 1 - config.alpha
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"""
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if level is None:
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level = 1 - config.alpha
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# For a given lower limit, we can compute the corresponding level*100% interval
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def interval_width(lower):
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upper = distribution.ppf(distribution.cdf(lower) + level)
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return upper - lower
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@ -26,6 +26,7 @@ from datetime import datetime
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import itertools
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from .bayes_factors import BayesFactor, bayesfactor_afbf
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from .config import config
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from .sig_tests import FTestResult
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from .utils import Estimate, check_nan, cols_for_formula, fmt_p, formula_factor_ref_category, parse_patsy_term
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@ -220,7 +221,7 @@ class RegressionResult:
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out += '</table>'
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# Render coefficients table
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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>')
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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)
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for term_name, term in self.terms.items():
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if isinstance(term, SingleTerm):
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@ -306,7 +307,7 @@ class RegressionResult:
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raise Exception('Attempt to render unknown term type')
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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
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table2_text = table2.as_text().replace(' \ue000 ', '(95% CI)') # Render heading in the right spot
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table2_text = table2.as_text().replace(' \ue000 ', '({:g}% CI)'.format((1-config.alpha)*100)) # Render heading in the right spot
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table2_lines = table2_text.splitlines(keepends=False)
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# Render divider line between 2 tables
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@ -382,7 +383,7 @@ def regress(
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# Process terms
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terms = {}
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confint = result.conf_int()
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confint = result.conf_int(config.alpha)
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for raw_name, raw_beta in result.params.items():
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beta = Estimate(raw_beta, confint[0][raw_name], confint[1][raw_name])
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@ -466,9 +467,10 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
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# Transfer other parameters to R
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lc['formula_'] = self.formula
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lc['alpha'] = config.alpha
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# Fit the model
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model = ro.r('logistf(formula_, data=df)')
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model = ro.r('logistf(formula_, data=df, alpha=alpha)')
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# TODO: Handle categorical terms?
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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'])}
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@ -22,6 +22,7 @@ import statsmodels.api as sm
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import functools
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import warnings
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from .config import config
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from .utils import Estimate, as_2groups, check_nan, fmt_p
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# ----------------
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@ -42,10 +43,10 @@ class TTestResult:
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self.delta_direction = delta_direction
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def _repr_html_(self):
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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)
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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)
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def summary(self):
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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)
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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)
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def ttest_ind(df, dep, ind, *, nan_policy='warn'):
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"""Perform an independent-sample Student's t test"""
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@ -65,7 +66,7 @@ def ttest_ind(df, dep, ind, *, nan_policy='warn'):
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statistic, pvalue, dof = cm.ttest_ind()
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delta = d1.mean - d2.mean
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ci0, ci1 = cm.tconfint_diff()
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ci0, ci1 = cm.tconfint_diff(config.alpha)
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# t test is symmetric so take absolute values
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return TTestResult(
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@ -209,16 +210,16 @@ class PearsonChiSquaredResult:
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def _repr_html_(self):
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if self.oddsratio is not None:
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return '{}<br><i>χ</i><sup>2</sup>({}) = {:.2f}; <i>p</i> {}<br>OR (95% CI) = {}<br>RR (95% CI) = {}'.format(
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self.ct._repr_html_(), self.dof, self.statistic, fmt_p(self.pvalue, html=True), self.oddsratio.summary(), self.riskratio.summary())
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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(
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self.ct._repr_html_(), self.dof, self.statistic, fmt_p(self.pvalue, html=True), (1-config.alpha)*100, self.oddsratio.summary(), self.riskratio.summary())
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else:
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return '{}<br><i>χ</i><sup>2</sup>({}) = {:.2f}; <i>p</i> {}'.format(
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self.ct._repr_html_(), self.dof, self.statistic, fmt_p(self.pvalue, html=True))
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def summary(self):
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if self.oddsratio is not None:
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return '{}\nχ²({}) = {:.2f}; p {}\nOR (95% CI) = {}\nRR (95% CI) = {}'.format(
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self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False), self.oddsratio.summary(), self.riskratio.summary())
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return '{0}\nχ²({1}) = {2:.2f}; p {3}\nOR ({4:g}% CI) = {5}\nRR ({4:g}% CI) = {6}'.format(
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self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False), (1-config.alpha)*100, self.oddsratio.summary(), self.riskratio.summary())
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else:
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return '{}\nχ²({}) = {:.2f}; p {}'.format(
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self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False))
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@ -247,8 +248,8 @@ def chi2(df, dep, ind, *, nan_policy='warn'):
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smct = sm.stats.Table2x2(np.flip(ct.to_numpy()), shift_zeros=False)
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result = smct.test_nominal_association()
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ORci = smct.oddsratio_confint()
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RRci = smct.riskratio_confint()
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ORci = smct.oddsratio_confint(config.alpha)
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RRci = smct.riskratio_confint(config.alpha)
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return PearsonChiSquaredResult(
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ct=ct, statistic=result.statistic, dof=result.df, pvalue=result.pvalue,
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@ -272,10 +273,10 @@ class PearsonRResult:
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self.pvalue = pvalue
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def _repr_html_(self):
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return '<i>r</i> (95% CI) = {}; <i>p</i> {}'.format(self.statistic.summary(), fmt_p(self.pvalue, html=True))
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return '<i>r</i> ({:g}% CI) = {}; <i>p</i> {}'.format((1-config.alpha)*100, self.statistic.summary(), fmt_p(self.pvalue, html=True))
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def summary(self):
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return 'r (95% CI) = {}; p {}'.format(self.statistic.summary(), fmt_p(self.pvalue, html=False))
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return 'r ({:g}% CI) = {}; p {}'.format((1-config.alpha)*100, self.statistic.summary(), fmt_p(self.pvalue, html=False))
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def pearsonr(df, dep, ind, *, nan_policy='warn'):
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"""Compute the Pearson correlation coefficient (Pearson's r)"""
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