def test_spearmanr(self): "Testing spearmanr" data1 = [self.L, self.A] data2 = [self.M, self.B] results = (0.93233082706766912, 2.2066972068439387e-009) i = 0 for d in data1: self.assertEqual(stats.spearmanr(d, data2[i])[i], results[i]) i += 1
def test_spearmanr(self): "Testing spearmanr" data1 = [ self.L, self.A ] data2 = [ self.M, self.B ] results = (0.93233082706766912, 2.2066972068439387e-009) i = 0 for d in data1: self.assertEqual( stats.spearmanr( d, data2[i] )[i], results[i] ) i += 1
b = N.array(m) pb = [0] * 9 + [1] * 11 apb = N.array(pb) print('paired:') #stats.paired(l,m) #stats.paired(a,b) print() print() print('pearsonr:') print(stats.pearsonr(l, m)) print(stats.pearsonr(a, b)) print('spearmanr:') print(stats.spearmanr(l, m)) print(stats.spearmanr(a, b)) print('pointbiserialr:') print(stats.pointbiserialr(pb, l)) print(stats.pointbiserialr(apb, a)) print('kendalltau:') print(stats.kendalltau(l, m)) print(stats.kendalltau(a, b)) print('linregress:') print(stats.linregress(l, m)) print(stats.linregress(a, b)) print('\nINFERENTIAL') print('ttest_1samp:') print(stats.ttest_1samp(l, 12)) print(stats.ttest_1samp(a, 12))
def evaluate( self, *args, **params): return _stats.spearmanr(*args, **params)
b = N.array(m) pb = [0]*9 + [1]*11 apb = N.array(pb) print 'paired:' #stats.paired(l,m) #stats.paired(a,b) print print print 'pearsonr:' print stats.pearsonr(l,m) print stats.pearsonr(a,b) print 'spearmanr:' print stats.spearmanr(l,m) print stats.spearmanr(a,b) print 'pointbiserialr:' print stats.pointbiserialr(pb,l) print stats.pointbiserialr(apb,a) print 'kendalltau:' print stats.kendalltau(l,m) print stats.kendalltau(a,b) print 'linregress:' print stats.linregress(l,m) print stats.linregress(a,b) print '\nINFERENTIAL' print 'ttest_1samp:' print stats.ttest_1samp(l,12) print stats.ttest_1samp(a,12)
stats.attest_rel(array(before), array(after), 1, 'Before', 'After') print "\n\nPearson's r" x = [0, 0, 1, 1, 1, 2, 2, 3, 3, 4] y = [8, 7, 7, 6, 5, 4, 4, 4, 2, 0] print 'SHOULD BE -0.94535 (N=10) ... Basic Stats 1st ed, p.190' print stats.pearsonr(x, y) print stats.apearsonr(array(x), array(y)) print "\n\nSpearman's r" x = [4, 1, 9, 8, 3, 5, 6, 2, 7] y = [3, 2, 8, 6, 5, 4, 7, 1, 9] print '\nSHOULD BE +0.85 on the dot (N=9) ... Basic Stats 1st ed, p.193' print stats.spearmanr(x, y) print stats.aspearmanr(array(x), array(y)) print '\n\nPoint-Biserial r' gender = [1, 1, 1, 1, 2, 2, 2, 2, 2, 2] score = [35, 38, 41, 40, 60, 65, 65, 68, 68, 64] print '\nSHOULD BE +0.981257 (N=10) ... Basic Stats 1st ed, p.197' print stats.pointbiserialr(gender, score) print stats.apointbiserialr(array(gender), array(score)) print '\n\nLinear Regression' x = [1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4] y = [2, 4, 4, 6, 2, 4, 7, 8, 6, 8, 7] print '\nSHOULD BE 1.44, 1.47, 0.736, ???, 1.42 (N=11)... Basic Stats 1st ed, p.211-2'
print "\n\nPearson's r" x = [0,0,1,1,1,2,2,3,3,4] y = [8,7,7,6,5,4,4,4,2,0] print 'SHOULD BE -0.94535 (N=10) ... Basic Stats 1st ed, p.190' print stats.pearsonr(x,y) print stats.apearsonr(array(x),array(y)) print "\n\nSpearman's r" x = [4,1,9,8,3,5,6,2,7] y = [3,2,8,6,5,4,7,1,9] print '\nSHOULD BE +0.85 on the dot (N=9) ... Basic Stats 1st ed, p.193' print stats.spearmanr(x,y) print stats.aspearmanr(array(x),array(y)) print '\n\nPoint-Biserial r' gender = [1,1,1,1,2,2,2,2,2,2] score = [35, 38, 41, 40, 60, 65, 65, 68, 68, 64] print '\nSHOULD BE +0.981257 (N=10) ... Basic Stats 1st ed, p.197' print stats.pointbiserialr(gender,score) print stats.apointbiserialr(array(gender),array(score)) print '\n\nLinear Regression' x = [1,1,2,2,2,3,3,3,4,4,4]
b = N.array(m) pb = [0] * 9 + [1] * 11 apb = N.array(pb) print 'paired:' #stats.paired(l,m) #stats.paired(a,b) print print print 'pearsonr:' print stats.pearsonr(l, m) print stats.pearsonr(a, b) print 'spearmanr:' print stats.spearmanr(l, m) print stats.spearmanr(a, b) print 'pointbiserialr:' print stats.pointbiserialr(pb, l) print stats.pointbiserialr(apb, a) print 'kendalltau:' print stats.kendalltau(l, m) print stats.kendalltau(a, b) print 'linregress:' print stats.linregress(l, m) print stats.linregress(a, b) print '\nINFERENTIAL' print 'ttest_1samp:' print stats.ttest_1samp(l, 12) print stats.ttest_1samp(a, 12)
print("\n\nPearson's r") x = [0,0,1,1,1,2,2,3,3,4] y = [8,7,7,6,5,4,4,4,2,0] print('SHOULD BE -0.94535 (N=10) ... Basic Stats 1st ed, p.190') print(stats.pearsonr(x,y)) print(stats.apearsonr(array(x),array(y))) print("\n\nSpearman's r") x = [4,1,9,8,3,5,6,2,7] y = [3,2,8,6,5,4,7,1,9] print('\nSHOULD BE +0.85 on the dot (N=9) ... Basic Stats 1st ed, p.193') print(stats.spearmanr(x,y)) print(stats.aspearmanr(array(x),array(y))) print('\n\nPoint-Biserial r') gender = [1,1,1,1,2,2,2,2,2,2] score = [35, 38, 41, 40, 60, 65, 65, 68, 68, 64] print('\nSHOULD BE +0.981257 (N=10) ... Basic Stats 1st ed, p.197') print(stats.pointbiserialr(gender,score)) print(stats.apointbiserialr(array(gender),array(score))) print('\n\nLinear Regression') x = [1,1,2,2,2,3,3,3,4,4,4]