# -*- coding: utf-8 -*-
"""Base classes for statistical test results
Created on Mon Apr 22 14:03:21 2013
Author: Josef Perktold
"""
from statsmodels.compat.python import lzip, zip
import numpy as np
[docs]class AllPairsResults(object):
'''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 : string
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 : None or list of strings
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).
'''
[docs] 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 = (1. + np.sqrt(1 + 8 * len(all_pairs))) * 0.5
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 = ['%r' % (pairs,) for pairs in all_pairs]
else:
self.all_pairs_names = ['%s-%s' % (levels[pairs[0]],
levels[pairs[1]])
for pairs in all_pairs]
[docs] 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()
[docs] 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 don't assume we have all pairs
pvals_mat[lzip(*self.all_pairs)] = self.pval_corrected()
#pvals_mat[np.triu_indices(k, 1)] = self.pval_corrected()
return pvals_mat
[docs] 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(('%s %6.4g' % (pairs, pv) for (pairs, pv) in
zip(self.all_pairs_names, self.pval_corrected())))
return text