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