Add documentation for RegressionResult.brant
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@ -6,17 +6,20 @@ Functions
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.. autofunction:: yli.logit_then_regress
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.. autoclass:: yli.OrdinalLogit
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.. autoclass:: yli.PenalisedLogit
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.. autofunction:: yli.regress
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.. autofunction:: yli.regress_bootstrap
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.. autofunction:: yli.vif
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Result classes
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--------------
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.. autoclass:: yli.regress.BrantResult
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:members:
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.. autoclass:: yli.regress.CategoricalTerm
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:members:
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@ -256,7 +256,33 @@ class RegressionResult:
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return bf01.invert()
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def brant(self):
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# TODO: Documentation
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"""
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Perform the Brant test for the parallel regression assumption in ordinal regression
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Applicable when the model was fitted using :class:`OrdinalLogit`.
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:rtype: :class:`BrantResult`
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**Example:**
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.. code-block::
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df = pd.DataFrame(...)
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model = yli.regress(yli.OrdinalLogit, df, 'apply', 'pared + public + gpa', exp=False)
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model.brant()
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.. code-block:: text
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χ² df p
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Omnibus 4.34 3 0.23
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pared 0.13 1 0.72
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public 3.44 1 0.06
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gpa 0.18 1 0.67
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The output shows the result of the Brant test. For example, for the omnibus test of the parallel regression assumption across all independent variables, the *χ*:sup:`2` statistic is 4.34, the *χ*:sup:`2` distribution has 3 degrees of freedom, and the test is not significant, with *p* value 0.23.
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**Reference:** Brant R. Assessing proportionality in the proportional odds model for ordinal logistic regression. *Biometrics*. 1990;46(4):1171–8. `doi:10.2307/2532457 <https://doi.org/10.2307/2532457>`_
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"""
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df = self.df()
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if df is None:
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@ -1070,7 +1096,11 @@ def _wald_test(param_names, params, formula, vcov, df):
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return lmr.wald_test(formula, cov_p=vcov, use_f=False, scalar=True)
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class BrantResult:
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# TODO: Documentation
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"""
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Result of a Brant test for ordinal regression
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See :meth:`RegressionResult.brant`.
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"""
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def __init__(self, tests):
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#: Results for Brant test on each coefficient (*Dict[str, ChiSquaredResult]*)
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