def mann_white(venom_vals_list,others_gp_vals): '''compares one mean to another are they signif different takes a two list of floats for each group Mann-... U test for significant difference between two groups\ >>> >>>mann_white(list_venom_dnds_as_floats,list_comparable_gp_vals) ...Mean venom dnds = 0.470993103448, mean others 0.292471404883. ...Mann-whitney test U = 205437.0 with a p(one tailed) = 2.42740449563e-06 >>> One-sided p-value assuming a asymptotic normal distribution. Notes Use only when the number of observation in each sample is > 20 and you have 2 independent samples of ranks. Mann-Whitney U is significant if the u-obtained is LESS THAN or equal to the critical value of U. This test corrects for ties and by default uses a continuity correction . The reported p-value is for a one-sided hypothesis, to get the two-sided p-value multiply the returned p-value by 2.''' test_gp = [vals for vals in venom_vals_list] #redundent but helps understanding other_gp = [vals for vals in others_gp_vals] result = stats.mannwhitneyu(test_gp,other_gp) test_mean = stats.mean(test_gp) other_mean = stats.mean(other_gp) return "Mean venom dnds = %s, mean others %s.Mann-whitney test U = %s with a p(one tailed) = %s"\ %(test_mean,other_mean,result[0],result[1])
def test_mannwhitneyu(self): "Testing mannwhitneyu" data1 = [self.L, self.A] data2 = [self.M, self.B] results = (138.0, 0.046699380915068867) i = 0 for d in data1: self.assertEqual(stats.mannwhitneyu(d, data2[i])[i], results[i]) i += 1
def test_mannwhitneyu(self): "Testing mannwhitneyu" data1 = [ self.L, self.A ] data2 = [ self.M, self.B ] results = (138.0, 0.046699380915068867) i = 0 for d in data1: self.assertEqual( stats.mannwhitneyu( d, data2[i] )[i], results[i] ) i += 1
print(stats.ttest_1samp(a, 12)) print('ttest_ind:') print(stats.ttest_ind(l, m)) print(stats.ttest_ind(a, b)) print('ttest_rel:') print(stats.ttest_rel(l, m)) 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 stats.ttest_1samp(a,12) print 'ttest_ind:' print stats.ttest_ind(l,m) print stats.ttest_ind(a,b) print 'ttest_rel:' print stats.ttest_rel(l,m) 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 '\n\nChi-Square' fo = [10, 40] print '\nSHOULD BE 18.0, <<<0.01 (df=1) ... Basic Stats 1st ed. p.457' print stats.chisquare(fo) print stats.achisquare(array(fo)) print '\nSHOULD BE 5.556, 0.01<p<0.05 (df=1) ... Basic Stats 1st ed. p.460' print stats.chisquare(fo, [5, 45]) print stats.achisquare(array(fo), array([5, 45], 'f')) print '\n\nMann Whitney U' red = [540, 480, 600, 590, 605] black = [760, 890, 1105, 595, 940] print '\nSHOULD BE 2.0, 0.01<p<0.05 (N=5,5) ... Basic Stats 1st ed, p.473-4' 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)
def evaluate( self, *args, **params): return _stats.mannwhitneyu(*args, **params)
fo = [10,40] print '\nSHOULD BE 18.0, <<<0.01 (df=1) ... Basic Stats 1st ed. p.457' print stats.chisquare(fo) print stats.achisquare(array(fo)) print '\nSHOULD BE 5.556, 0.01<p<0.05 (df=1) ... Basic Stats 1st ed. p.460' print stats.chisquare(fo,[5,45]) print stats.achisquare(array(fo),array([5,45],'f')) print '\n\nMann Whitney U' red = [540,480,600,590,605] black = [760,890,1105,595,940] print '\nSHOULD BE 2.0, 0.01<p<0.05 (N=5,5) ... Basic Stats 1st ed, p.473-4' 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.ttest_1samp(a, 12) print 'ttest_ind:' print stats.ttest_ind(l, m) print stats.ttest_ind(a, b) print 'ttest_rel:' print stats.ttest_rel(l, m) 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)
fo = [10,40] print('\nSHOULD BE 18.0, <<<0.01 (df=1) ... Basic Stats 1st ed. p.457') print(stats.chisquare(fo)) print(stats.achisquare(array(fo))) print('\nSHOULD BE 5.556, 0.01<p<0.05 (df=1) ... Basic Stats 1st ed. p.460') print(stats.chisquare(fo,[5,45])) print(stats.achisquare(array(fo),array([5,45],'f'))) print('\n\nMann Whitney U') red = [540,480,600,590,605] black = [760,890,1105,595,940] print('\nSHOULD BE 2.0, 0.01<p<0.05 (N=5,5) ... Basic Stats 1st ed, p.473-4') 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')