Add auto_univariable
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@ -6,6 +6,8 @@ Functions
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.. autofunction:: yli.anova_oneway
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.. autofunction:: yli.auto_univariable
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.. autofunction:: yli.chi2
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.. autofunction:: yli.mannwhitney
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@ -17,6 +19,9 @@ Functions
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Result classes
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--------------
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.. autoclass:: yli.sig_tests.AutoBinaryResult
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:members:
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.. autoclass:: yli.sig_tests.BrunnerMunzelResult
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:members:
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@ -19,7 +19,7 @@ from .config import config
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from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
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from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
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from .regress import PenalisedLogit, logit_then_regress, regress, vif
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from .sig_tests import anova_oneway, chi2, mannwhitney, pearsonr, ttest_ind
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from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, ttest_ind
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def reload_me():
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import importlib
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179
yli/sig_tests.py
179
yli/sig_tests.py
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@ -98,6 +98,18 @@ class TTestResult:
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"""
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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)
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def summary_short(self, html):
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"""
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Return a stringified summary of the *t* test (*t* statistic only)
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:rtype: str
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"""
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if html:
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return '<i>t</i>({:.0f}) = {:.2f}'.format(self.dof, self.statistic)
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else:
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return 't({:.0f}) = {:.2f}'.format(self.dof, self.statistic)
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def ttest_ind(df, dep, ind, *, nan_policy='warn'):
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"""
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@ -336,6 +348,18 @@ class MannWhitneyResult:
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return line1 + '\n' + self.brunnermunzel.summary()
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else:
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return line1
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def summary_short(self, html):
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"""
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Return a stringified summary of the Mann–Whitney test (*U* statistic only)
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:rtype: str
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"""
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if html:
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return '<i>U</i> = {:.1f}'.format(self.statistic)
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else:
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return 'U = {:.1f}'.format(self.statistic)
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class BrunnerMunzelResult:
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"""
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@ -422,7 +446,7 @@ def mannwhitney(df, dep, ind, *, nan_policy='warn', brunnermunzel=True, use_cont
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# Check for/clean NaNs
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df = check_nan(df[[ind, dep]], nan_policy)
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# Convert pandas nullable types for independent variables as this breaks statsmodels
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# Convert pandas nullable types for independent variables as this breaks mannwhitneyu
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df = convert_pandas_nullable(df)
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# Ensure 2 groups for ind
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@ -506,6 +530,18 @@ class PearsonChiSquaredResult:
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else:
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return '{}\n\nχ²({}) = {:.2f}; p {}'.format(
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self.ct, self.dof, self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION))
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def summary_short(self, html):
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"""
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Return a stringified summary of the *χ*:sup:`2` test (*χ*:sup:`2` statistic only)
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:rtype: str
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"""
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if html:
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return '<i>χ</i><sup>2</sup>({}) = {:.2f}'.format(self.dof, self.statistic)
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else:
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return 'χ²({}) = {:.2f}'.format(self.dof, self.statistic)
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def chi2(df, dep, ind, *, nan_policy='warn'):
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"""
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@ -662,3 +698,144 @@ def pearsonr(df, dep, ind, *, nan_policy='warn'):
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ci = result.confidence_interval()
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return PearsonRResult(statistic=Estimate(result.statistic, ci.low, ci.high), pvalue=result.pvalue)
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# ----------------------------
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# Automatic selection of tests
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class AutoBinaryResult:
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"""
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Result of automatically computed univariable tests of association for a dichotomous dependent variable
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See :func:`yli.auto_univariable`.
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Results data stored within instances of this class is not intended to be directly accessed.
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"""
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def __init__(self, *, dep, group1, group2, result_data, result_labels):
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#: Name of the dependent variable (*str*)
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self.dep = dep
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#: Name of the first group (*str*)
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self.group1 = group1
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#: Name of the second group (*str*)
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self.group2 = group2
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# List of tuples (first group summary, second group summary, test result)
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self._result_data = result_data
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# List of row labels for the independente variables
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self._result_labels = result_labels
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def __repr__(self):
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if config.repr_is_summary:
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return self.summary()
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return super().__repr__()
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def _repr_html_(self):
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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)
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for data, label in zip(self._result_data, self._result_labels):
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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))
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result += '</tbody></table>'
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return result
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def summary(self):
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"""
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Return a stringified summary of the tests of association
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:rtype: str
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"""
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# Format data for output
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result_data_fmt = [
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[r[0], r[1], r[2].summary_short(False), fmt_p(r[2].pvalue, PValueStyle.TABULAR)]
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for r in self._result_data
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]
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result_labels_fmt = [r[0] for r in self._result_labels]
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table = pd.DataFrame(result_data_fmt, index=result_labels_fmt, columns=pd.Index([self.group1, self.group2, '', 'p'], name=self.dep))
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return str(table)
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def auto_univariable(df, dep, inds, *, ordinal=[], nan_policy='warn'):
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"""
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Automatically compute univariable tests of association for a dichotomous dependent variable
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The tests performed are:
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* For a dichotomous independent variable – :func:`yli.chi2`
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* For a continuous independent variable – :func:`yli.ttest_ind`
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* For an ordinal independent variable – :func:`yli.mannwhitney`
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:param df: Data to perform the test on
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:type df: DataFrame
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:param dep: Column in *df* for the dependent variable (dichotomous)
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:type dep: str
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:param inds: Columns in *df* for the independent variables
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:type inds: List[str]
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:param ordinal: Columns in *df* to treat as ordinal rather than continuous
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:type ordinal: List[str]
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:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
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:type nan_policy: str
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:rtype: :class:`yli.sig_tests.AutoBinaryResult`
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"""
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# Check for/clean NaNs
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# Following this, we pass nan_policy='raise' to assert no NaNs remaining
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df = check_nan(df[inds + [dep]], nan_policy)
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# Ensure 2 groups for dep
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# TODO: Work for non-binary dependent variables?
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group1, data1, group2, data2 = as_2groups(df, inds, dep)
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result_data = []
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result_labels = []
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for ind in inds:
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if df[ind].dtype in ('bool', 'category', 'object'):
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# Pearson chi-squared test
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result = chi2(df, dep, ind, nan_policy='raise')
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values = sorted(df[ind].unique())
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# Value counts
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result_labels.append((
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'{}, {}'.format(ind, ':'.join(str(v) for v in values)),
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'{}, {}'.format(ind, ':'.join(str(v) for v in values)),
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))
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result_data.append((
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':'.join(str((data1[ind] == v).sum()) for v in values),
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':'.join(str((data2[ind] == v).sum()) for v in values),
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result
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))
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elif df[ind].dtype in ('float64', 'int64', 'Float64', 'Int64'):
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if ind in ordinal:
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# Mann-Whitney test
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result = mannwhitney(df, ind, dep, nan_policy='raise')
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result_labels.append((
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'{}, median (IQR)'.format(ind),
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'{}, median (IQR)'.format(ind),
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))
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result_data.append((
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'{:.2f} ({})'.format(result.med1, result.iqr1.summary()),
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'{:.2f} ({})'.format(result.med2, result.iqr2.summary()),
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result
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))
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else:
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# t test
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result = ttest_ind(df, ind, dep, nan_policy='raise')
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result_labels.append((
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'{}, μ (SD)'.format(ind),
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'{}, <i>μ</i> (SD)'.format(ind),
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))
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result_data.append((
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'{:.2f} ({:.2f})'.format(result.mu1, result.sd1),
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'{:.2f} ({:.2f})'.format(result.mu2, result.sd2),
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result
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))
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else:
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raise Exception('Unsupported independent dtype for auto_univariable, {}'.format(df[ind].dtype))
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return AutoBinaryResult(dep=dep, group1=group1, group2=group2, result_data=result_data, result_labels=result_labels)
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