some new features

.venv/lib/python3.12/site-packages/statsmodels/stats/base.py

@@ -0,0 +1,128 @@
+"""Base classes for statistical test results
+
+Created on Mon Apr 22 14:03:21 2013
+
+Author: Josef Perktold
+"""
+from statsmodels.compat.python import lzip
+import numpy as np
+from statsmodels.tools.testing import Holder
+
+
+class HolderTuple(Holder):
+    """Holder class with indexing
+
+    """
+
+    def __init__(self, tuple_=None, **kwds):
+        super().__init__(**kwds)
+        if tuple_ is not None:
+            self.tuple = tuple(getattr(self, att) for att in tuple_)
+        else:
+            self.tuple = (self.statistic, self.pvalue)
+
+    def __iter__(self):
+        yield from self.tuple
+
+    def __getitem__(self, idx):
+        return self.tuple[idx]
+
+    def __len__(self):
+        return len(self.tuple)
+
+    def __array__(self, dtype=None, copy=True):
+        return np.array(list(self.tuple), dtype=dtype, copy=copy)
+
+
+class AllPairsResults:
+    '''Results class for pairwise comparisons, based on p-values
+
+    Parameters
+    ----------
+    pvals_raw : array_like, 1-D
+        p-values from a pairwise comparison test
+    all_pairs : list of tuples
+        list of indices, one pair for each comparison
+    multitest_method : str
+        method that is used by default for p-value correction. This is used
+        as default by the methods like if the multiple-testing method is not
+        specified as argument.
+    levels : {list[str], None}
+        optional names of the levels or groups
+    n_levels : None or int
+        If None, then the number of levels or groups is inferred from the
+        other arguments. It can be explicitly specified, if the inferred
+        number is incorrect.
+
+    Notes
+    -----
+    This class can also be used for other pairwise comparisons, for example
+    comparing several treatments to a control (as in Dunnet's test).
+
+    '''
+
+    def __init__(self, pvals_raw, all_pairs, multitest_method='hs',
+                 levels=None, n_levels=None):
+        self.pvals_raw = pvals_raw
+        self.all_pairs = all_pairs
+        if n_levels is None:
+            # for all_pairs nobs*(nobs-1)/2
+            self.n_levels = np.max(all_pairs) + 1
+        else:
+            self.n_levels = n_levels
+
+        self.multitest_method = multitest_method
+        self.levels = levels
+        if levels is None:
+            self.all_pairs_names = [
+                f'{pairs!r}' for pairs in all_pairs
+            ]
+        else:
+            self.all_pairs_names = [
+                '{}-{}'.format(
+                    levels[pairs[0]], levels[pairs[1]]
+                ) for pairs in all_pairs
+            ]
+
+    def pval_corrected(self, method=None):
+        '''p-values corrected for multiple testing problem
+
+        This uses the default p-value correction of the instance stored in
+        ``self.multitest_method`` if method is None.
+
+        '''
+        import statsmodels.stats.multitest as smt
+        if method is None:
+            method = self.multitest_method
+        # TODO: breaks with method=None
+        return smt.multipletests(self.pvals_raw, method=method)[1]
+
+    def __str__(self):
+        return self.summary()
+
+    def pval_table(self):
+        '''create a (n_levels, n_levels) array with corrected p_values
+
+        this needs to improve, similar to R pairwise output
+        '''
+        k = self.n_levels
+        pvals_mat = np.zeros((k, k))
+        # if we do not assume we have all pairs
+        pvals_mat[lzip(*self.all_pairs)] = self.pval_corrected()
+        return pvals_mat
+
+    def summary(self):
+        '''returns text summarizing the results
+
+        uses the default pvalue correction of the instance stored in
+        ``self.multitest_method``
+        '''
+        import statsmodels.stats.multitest as smt
+        maxlevel = max(len(ss) for ss in self.all_pairs_names)
+
+        text = ('Corrected p-values using %s p-value correction\n\n'
+                % smt.multitest_methods_names[self.multitest_method])
+        text += 'Pairs' + (' ' * (maxlevel - 5 + 1)) + 'p-values\n'
+        text += '\n'.join(f'{pairs}  {pv:6.4g}' for (pairs, pv) in
+                          zip(self.all_pairs_names, self.pval_corrected()))
+        return text