def test_wilcoxont(self): "Testing wilcoxont" data1 = [ self.L, self.A ] data2 = [ self.M, self.B ] results = (0.0, 8.8574167624866362e-005) i = 0 for d in data1: self.assertEqual( stats.wilcoxont( d, data2[i] )[i], results[i] ) i += 1
def test_wilcoxont(self): "Testing wilcoxont" data1 = [self.L, self.A] data2 = [self.M, self.B] results = (0.0, 8.8574167624866362e-005) i = 0 for d in data1: self.assertEqual(stats.wilcoxont(d, data2[i])[i], results[i]) i += 1
print(stats.ttest_rel(a, b)) print('chisquare:') print(stats.chisquare(l)) print(stats.chisquare(a)) print('ks_2samp:') print(stats.ks_2samp(l, m)) print(stats.ks_2samp(a, b)) print('mannwhitneyu:') print(stats.mannwhitneyu(l, m)) print(stats.mannwhitneyu(a, b)) print('ranksums:') print(stats.ranksums(l, m)) print(stats.ranksums(a, b)) print('wilcoxont:') print(stats.wilcoxont(l, m)) print(stats.wilcoxont(a, b)) print('kruskalwallish:') print(stats.kruskalwallish(l, m, l)) print(len(l), len(m)) print(stats.kruskalwallish(a, b, a)) print('friedmanchisquare:') print(stats.friedmanchisquare(l, m, l)) print(stats.friedmanchisquare(a, b, a)) print('\nANOVAs') #execfile('test_anova.py') l = list(range(1, 21)) a = N.array(l) ll = [l] * 5
print stats.ttest_rel(a,b) print 'chisquare:' print stats.chisquare(l) print stats.chisquare(a) print 'ks_2samp:' print stats.ks_2samp(l,m) print stats.ks_2samp(a,b) print 'mannwhitneyu:' print stats.mannwhitneyu(l,m) print stats.mannwhitneyu(a,b) print 'ranksums:' print stats.ranksums(l,m) print stats.ranksums(a,b) print 'wilcoxont:' print stats.wilcoxont(l,m) print stats.wilcoxont(a,b) print 'kruskalwallish:' print stats.kruskalwallish(l,m,l) print len(l), len(m) print stats.kruskalwallish(a,b,a) print 'friedmanchisquare:' print stats.friedmanchisquare(l,m,l) print stats.friedmanchisquare(a,b,a) print '\nANOVAs' #execfile('test_anova.py') l = range(1,21) a = N.array(l) ll = [l]*5
print stats.mannwhitneyu(red, black) print stats.amannwhitneyu(array(red), array(black)) print '\n\nRank Sums' #(using red and black from above) print '\nSHOULD BE -2.19, p<0.0286 (slightly) ... Basic Stats 1st ed, p.474-5' print stats.ranksums(red, black) print stats.aranksums(N.array(red), N.array(black)) print '\n\nWilcoxon T' red = [540, 580, 600, 680, 430, 740, 600, 690, 605, 520] black = [760, 710, 1105, 880, 500, 990, 1050, 640, 595, 520] print '\nSHOULD BE +3.0, 0.01<p<0.05 (N=9) ... Basic Stats 1st ed, p.477-8' print stats.wilcoxont(red, black) print stats.awilcoxont(array(red), array(black)) print '\n\nKruskal-Wallis H' short = [10, 28, 26, 39, 6] medium = [24, 27, 35, 44, 58] tall = [68, 71, 57, 60, 62] print '\nSHOULD BE 9.62, p<0.01 (slightly) (df=2) ... Basic Stats 1st ed, p.478-9' print stats.kruskalwallish(short, medium, tall) print stats.akruskalwallish(array(short), array(medium), array(tall)) print '\n\nFriedman Chi Square' highman = [1, 1, 1, 1, 2, 1, 1, 1, 1, 2] shyman = [2, 3, 2, 3, 1, 3, 2, 3, 3, 1]
def evaluate( self, *args, **params): return _stats.wilcoxont(*args, **params)
print stats.amannwhitneyu(array(red),array(black)) print '\n\nRank Sums' #(using red and black from above) print '\nSHOULD BE -2.19, p<0.0286 (slightly) ... Basic Stats 1st ed, p.474-5' print stats.ranksums(red,black) print stats.aranksums(N.array(red),N.array(black)) print '\n\nWilcoxon T' red = [540,580, 600,680,430,740, 600,690,605,520] black = [760,710,1105,880,500,990,1050,640,595,520] print '\nSHOULD BE +3.0, 0.01<p<0.05 (N=9) ... Basic Stats 1st ed, p.477-8' print stats.wilcoxont(red,black) print stats.awilcoxont(array(red),array(black)) print '\n\nKruskal-Wallis H' short = [10,28,26,39,6] medium = [24,27,35,44,58] tall = [68,71,57,60,62] print '\nSHOULD BE 9.62, p<0.01 (slightly) (df=2) ... Basic Stats 1st ed, p.478-9' print stats.kruskalwallish(short,medium,tall) print stats.akruskalwallish(array(short),array(medium),array(tall)) print '\n\nFriedman Chi Square'
print stats.ttest_rel(a, b) print 'chisquare:' print stats.chisquare(l) print stats.chisquare(a) print 'ks_2samp:' print stats.ks_2samp(l, m) print stats.ks_2samp(a, b) print 'mannwhitneyu:' print stats.mannwhitneyu(l, m) print stats.mannwhitneyu(a, b) print 'ranksums:' print stats.ranksums(l, m) print stats.ranksums(a, b) print 'wilcoxont:' print stats.wilcoxont(l, m) print stats.wilcoxont(a, b) print 'kruskalwallish:' print stats.kruskalwallish(l, m, l) print len(l), len(m) print stats.kruskalwallish(a, b, a) print 'friedmanchisquare:' print stats.friedmanchisquare(l, m, l) print stats.friedmanchisquare(a, b, a) print '\nANOVAs' #execfile('test_anova.py') l = range(1, 21) a = N.array(l) ll = [l] * 5
print(stats.amannwhitneyu(array(red),array(black))) print('\n\nRank Sums') #(using red and black from above) print('\nSHOULD BE -2.19, p<0.0286 (slightly) ... Basic Stats 1st ed, p.474-5') print(stats.ranksums(red,black)) print(stats.aranksums(N.array(red),N.array(black))) print('\n\nWilcoxon T') red = [540,580, 600,680,430,740, 600,690,605,520] black = [760,710,1105,880,500,990,1050,640,595,520] print('\nSHOULD BE +3.0, 0.01<p<0.05 (N=9) ... Basic Stats 1st ed, p.477-8') print(stats.wilcoxont(red,black)) print(stats.awilcoxont(array(red),array(black))) print('\n\nKruskal-Wallis H') short = [10,28,26,39,6] medium = [24,27,35,44,58] tall = [68,71,57,60,62] print('\nSHOULD BE 9.62, p<0.01 (slightly) (df=2) ... Basic Stats 1st ed, p.478-9') print(stats.kruskalwallish(short,medium,tall)) print(stats.akruskalwallish(array(short),array(medium),array(tall))) print('\n\nFriedman Chi Square')