Add example code/output to documentation
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@ -24,6 +24,7 @@ from statsmodels.stats.outliers_influence import variance_inflation_factor
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from datetime import datetime
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import itertools
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import warnings
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from .bayes_factors import BayesFactor, bayesfactor_afbf
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from .config import config
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@ -39,8 +40,30 @@ def vif(df, formula=None, *, nan_policy='warn'):
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:param formula: If specified, calculate the VIF only for the variables in the formula
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:type formula: str
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:return: The variance inflation factor
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:rtype: float
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:return: The variance inflation factors
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:rtype: Series
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**Example:**
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.. code-block::
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df = pd.DataFrame({
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'D': [68.58, 67.33, 67.33, ...],
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'T1': [14, 10, 10, ...],
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'T2': [46, 73, 85, ...],
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...
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})
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yli.vif(df[['D', 'T1', 'T2', ...]])
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.. code-block:: text
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D 8.318301
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T1 6.081590
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T2 2.457122
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...
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dtype: float64
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The output shows the variance inflation factor for each variable in *df*.
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"""
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if formula:
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@ -444,6 +467,40 @@ def regress(
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:type exp: bool
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:rtype: :class:`yli.regress.RegressionResult`
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**Example:**
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.. code-block::
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df = pd.DataFrame({
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'Unhealthy': [False, False, False, ...],
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'Fibrinogen': [2.52, 2.46, 2.29, ...],
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'GammaGlobulin': [38, 36, 36, ...]
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})
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yli.regress(sm.Logit, df, 'Unhealthy', 'Fibrinogen + GammaGlobulin')
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.. code-block:: text
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Logistic Regression Results
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======================================================
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Dep. Variable: Unhealthy | No. Observations: 32
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Model: Logit | Df. Model: 2
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Method: MLE | Df. Residuals: 29
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Date: 2022-10-18 | Pseudo R²: 0.26
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Time: 19:00:34 | LL-Model: -11.47
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Std. Errors: Non-Robust | LL-Null: -15.44
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| p (LR): 0.02*
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======================================================
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exp(β) (95% CI) p
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-----------------------------------------------
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(Intercept) 0.00 (0.00 - 0.24) 0.03*
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Fibrinogen 6.80 (1.01 - 45.79) 0.049*
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GammaGlobulin 1.17 (0.92 - 1.48) 0.19
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-----------------------------------------------
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The output summarises the results of the regression.
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Note that the parameter estimates are automatically exponentiated.
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For example, the odds ratio for unhealthiness per unit increase in fibrinogen is 6.80, with 95% confidence interval 1.01–45.79, and is significant with *p* value 0.049.
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"""
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# Populate model_kwargs
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@ -603,6 +660,37 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
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Uses the R *logistf* library.
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This class should be used in conjunction with :func:`yli.regress`.
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**Example:**
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.. code-block::
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df = pd.DataFrame({
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'Pred': [1] * 20 + [0] * 220,
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'Outcome': [1] * 40 + [0] * 200
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})
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yli.regress(yli.PenalisedLogit, df, 'Outcome', 'Pred', exp=False)
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.. code-block:: text
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Penalised Logistic Regression Results
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=========================================================
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Dep. Variable: Outcome | No. Observations: 240
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Model: Logit | Df. Model: 1
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Method: Penalised ML | Pseudo R²: 0.37
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Date: 2022-10-19 | LL-Model: -66.43
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Time: 07:50:40 | LL-Null: -105.91
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Std. Errors: Non-Robust | p (LR): <0.001*
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=========================================================
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β (95% CI) p
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---------------------------------------------
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(Intercept) -2.28 (-2.77 - -1.85) <0.001*
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Pred 5.99 (3.95 - 10.85) <0.001*
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---------------------------------------------
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The output summarises the result of the regression.
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The summary table shows that a penalised method was applied.
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Note that because `exp=False` was passed, the parameter estimates are not automatically exponentiated.
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"""
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def fit(self, disp=False):
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@ -78,6 +78,24 @@ def ttest_ind(df, dep, ind, *, nan_policy='warn'):
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:type nan_policy: str
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:rtype: :class:`yli.sig_tests.TTestResult`
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**Example:**
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.. code-block::
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df = pd.DataFrame({
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'Type': ['Fresh'] * 10 + ['Stored'] * 10,
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'Potency': [10.2, 10.5, 10.3, ...]
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})
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yli.ttest_ind(df, 'Potency', 'Type')
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.. code-block:: text
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t(18) = 4.24; p < 0.001*
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Δμ (95% CI) = 0.54 (0.27–0.81), Fresh > Stored
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The output states that the value of the *t* statistic is 4.24, the *t* distribution has 18 degrees of freedom, and the test is significant with *p* value < 0.001.
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The mean difference is 0.54 in favour of the *Fresh* group, with 95% confidence interval 0.27–0.81.
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"""
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# Check for/clean NaNs
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@ -129,7 +147,7 @@ class FTestResult:
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return super().__repr__()
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def _repr_html_(self):
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return '<i>F</i>({}, {}) = {:.2f}; <i>p</i> {}'.format(self.dof1, self.dof2, self.statistic, fmt_p(self.pvalue, html=True))
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return '<i>F</i>({:.0f}, {:.0f}) = {:.2f}; <i>p</i> {}'.format(self.dof1, self.dof2, self.statistic, fmt_p(self.pvalue, html=True))
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def summary(self):
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"""
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@ -138,7 +156,7 @@ class FTestResult:
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:rtype: str
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"""
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return 'F({}, {}) = {:.2f}; p {}'.format(self.dof1, self.dof2, self.statistic, fmt_p(self.pvalue, html=False))
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return 'F({:.0f}, {:.0f}) = {:.2f}; p {}'.format(self.dof1, self.dof2, self.statistic, fmt_p(self.pvalue, html=False))
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def anova_oneway(df, dep, ind, *, nan_policy='omit'):
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"""
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@ -154,6 +172,22 @@ def anova_oneway(df, dep, ind, *, nan_policy='omit'):
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:type nan_policy: str
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:rtype: :class:`yli.sig_tests.FTestResult`
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**Example:**
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.. code-block::
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df = pd.DataFrame({
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'Method': [1]*8 + [2]*7 + [3]*9,
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'Score': [96, 79, 91, ...]
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})
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yli.anova_oneway(df, 'Score', 'Method')
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.. code-block:: text
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F(2, 21) = 29.57; p < 0.001*
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The output states that the value of the *F* statistic is 29.57, the *F* distribution has 2 degrees of freedom in the numerator and 21 in the denominator, and the test is significant with *p* value < 0.001.
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"""
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# Check for/clean NaNs
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@ -182,7 +216,7 @@ class MannWhitneyResult:
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"""
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def __init__(self, statistic, pvalue, rank_biserial, direction, brunnermunzel=None):
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#: Mann–Whitney *U* statistic (*float*)
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#: Lesser of the two Mann–Whitney *U* statistics (*float*)
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self.statistic = statistic
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#: *p* value for the *U* statistic (*float*)
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self.pvalue = pvalue
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@ -212,7 +246,7 @@ class MannWhitneyResult:
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:rtype: str
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"""
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line1 = 'U = {:.1f}; p {}\nr = {}, {}'.format(self.statistic, fmt_p(self.pvalue, html=False), self.rank_biserial, self.direction)
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line1 = 'U = {:.1f}; p {}\nr = {:.2f}, {}'.format(self.statistic, fmt_p(self.pvalue, html=False), self.rank_biserial, self.direction)
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if self.brunnermunzel:
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return line1 + '\n' + self.brunnermunzel.summary()
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else:
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@ -273,6 +307,24 @@ def mannwhitney(df, dep, ind, *, nan_policy='warn', brunnermunzel=True, use_cont
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:param method: See *scipy.stats.mannwhitneyu*
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:rtype: :class:`yli.sig_tests.MannWhitneyResult`
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**Example:**
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.. code-block::
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df = pd.DataFrame({
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'Sample': ['Before'] * 12 + ['After'] * 12,
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'Oxygen': [11.0, 11.2, 11.2, ...]
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})
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yli.mannwhitney(df, 'Oxygen', 'Sample', method='asymptotic', alternative='less')
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.. code-block:: text
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U = 6.0; p < 0.001*
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r = 0.92, Before > After
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The output states that the value of the Mann–Whitney *U* statistic is 6.0, and the one-sided test is significant with asymptotic *p* value < 0.001.
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The rank-biserial correlation is 0.92 in favour of the *Before* group.
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"""
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# Check for/clean NaNs
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@ -352,10 +404,10 @@ class PearsonChiSquaredResult:
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"""
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if self.oddsratio is not None:
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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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return '{0}\n\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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return '{}\n\nχ²({}) = {:.2f}; p {}'.format(
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self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False))
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def chi2(df, dep, ind, *, nan_policy='warn'):
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@ -363,8 +415,8 @@ def chi2(df, dep, ind, *, nan_policy='warn'):
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Perform a Pearson *χ*:sup:`2` test
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If a 2×2 contingency table is obtained (i.e. if both variables are dichotomous), an odds ratio and risk ratio are calculated.
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The ratios are calculated for the higher-valued value in each variable (i.e. ``True`` compared with ``False`` for a boolean).
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The risk ratio is calculated relative to the independent variable.
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The ratios are calculated for the higher-valued value in each variable (i.e. *True* compared with *False* for a boolean).
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The risk ratio is calculated relative to the independent variable (rows of the contingency table).
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:param df: Data to perform the test on
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:type df: DataFrame
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@ -376,6 +428,33 @@ def chi2(df, dep, ind, *, nan_policy='warn'):
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:type nan_policy: str
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:rtype: :class:`yli.sig_tests.PearsonChiSquaredResult`
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**Example:**
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.. code-block::
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df = pd.DataFrame({
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'Response': np.repeat([False, True, False, True], [250, 750, 400, 1600]),
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'Stress': np.repeat([False, False, True, True], [250, 750, 400, 1600])
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})
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yli.chi2(df, 'Stress', 'Response')
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.. code-block:: text
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Stress False True
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Response
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False 250 400
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True 750 1600
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χ²(1) = 9.82; p = 0.002*
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OR (95% CI) = 1.33 (1.11–1.60)
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RR (95% CI) = 1.11 (1.03–1.18)
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The output shows the contingency table, and states that the value of the Pearson *χ*:sup:`2` statistic is 9.82, the *χ*:sup:`2` distribution has 1 degree of freedom, and the test is significant with *p* value 0.002.
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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.
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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.
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"""
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# Check for/clean NaNs
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@ -448,7 +527,7 @@ class PearsonRResult:
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def pearsonr(df, dep, ind, *, nan_policy='warn'):
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"""
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Compute the Pearson correlation coefficient (Pearson's r)
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Compute the Pearson correlation coefficient (Pearson's *r*)
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:param df: Data to perform the test on
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:type df: DataFrame
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