Ejemplos de wilcoxont en Python

Ejemplo n.º 1

Archivo: test_all.py Proyecto: avatarzhang/python-statlib

 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        

Ejemplo n.º 2

Archivo: test_all.py Proyecto: avatarzhang/python-statlib

    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

Ejemplo n.º 3

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

Ejemplo n.º 4

Archivo: test_allstats.py Proyecto: avatarzhang/python-statlib

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

Ejemplo n.º 5

Archivo: test_pairedstats.py Proyecto: haavee/RDBE

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]

Ejemplo n.º 6

Archivo: inferential.py Proyecto: debbiespiegel/salstat-statistics-package-2

 def evaluate( self, *args, **params):
     return _stats.wilcoxont(*args, **params)

Ejemplo n.º 7

Archivo: test_pairedstats.py Proyecto: avatarzhang/python-statlib

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'

Ejemplo n.º 8

Archivo: test_allstats.py Proyecto: haavee/RDBE

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

Ejemplo n.º 9

Archivo: test_pairedstats.py Proyecto: vsobolmaven/python-statlib

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')