Python pearsonr Examples

Example #1

File: recommender.py Project: MechanisM/assembleiaaberta

def pearson_correlation(v1,v2):

    try:
        pc= stats.pearsonr(v1,v2)[0]        
    except :
        pc= -1
    return (pc+1.0)/2.0

Example #2

File: data.py Project: regulomics/bnfinder

    def signs(self):
        """Calculates signs of correlation coefficients between a child and its parents
        """
        res={}
        for i,p in enumerate(self.parents):
	    try:
		sign = stats.pearsonr(self.data[i],self.data[-1])[0]
	    except ValueError:
		print self.data[i],self.data[-1]
            if sign >=0:
                res[p]="+"
            else:
                res[p]="-"
        return res

Example #3

File: utils.py Project: NabeelaMSIT/digichef

def pearson_correlation(v1,v2):
    '''>>> v1=[0,10,10,0,10]
       >>> v2=[10,0,0,10,0]
       >>> pearson_correlation(v1,v2)
       0.0
       >>> v2=v1
       >>> pearson_correlation(v1,v2)
       1.0
       >>> v2=[0,10,0,10,0]
       >>> pearson_correlation(v1,v2)
       0.41666666666666669
    '''
    try:
        pc= stats.pearsonr(v1,v2)[0]        
    except :
        pc= -1
    return (pc+1.0)/2.0

Example #4

def pearson_correlation(v1, v2):
    '''>>> v1=[0,10,10,0,10]
       >>> v2=[10,0,0,10,0]
       >>> pearson_correlation(v1,v2)
       0.0
       >>> v2=v1
       >>> pearson_correlation(v1,v2)
       1.0
       >>> v2=[0,10,0,10,0]
       >>> pearson_correlation(v1,v2)
       0.41666666666666669
    '''
    try:
        pc = stats.pearsonr(v1, v2)[0]
    except:
        pc = -1
    return (pc + 1.0) / 2.0

Example #5

 def tempfunc(lmbda, xvals, samps):
     y = boxcox(samps,lmbda)
     yvals = sort(y)
     r, prob  = stats.pearsonr(xvals, yvals)
     return 1-r

Example #6

 def tempfunc(shape, mi, yvals, func):
     xvals = func(mi, shape)
     r, prob = stats.pearsonr(xvals, yvals)
     return 1-r

Example #7

File: statstest.py Project: mozilla/jx-sqlite

aa = N.array(ll)

m = range(4,24)
m[10] = 34
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:')

Example #8

File: statstest.py Project: Delostik/common

m = range(4,24)
m[10] = 34 
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'

Example #9

m = range(4, 24)
m[10] = 34
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'

Example #10

File: testallstats.py Project: bennn/retic_performance

l = [float(f) for f in list(range(1,21))]
ll = [l]*5

m = dyn([float(f) for f in list(range(4,24))])
m[10] = 34.

pb = dyn([0.]*9 + [1.]*11)

print('paired:')
#stats.paired(l,m)
#stats.paired(l,l)

print()
print()
print('pearsonr:')
print(stats.pearsonr(l,m))
print(stats.pearsonr(l,l))
print('spearmanr:')
print('pointbiserialr:')
print(stats.pointbiserialr(pb,l))
print(stats.pointbiserialr(pb,l))
print('kendalltau:')
print(stats.kendalltau(l,m))
print(stats.kendalltau(l,l))
print('linregress:')
print(stats.linregress(l,m))
print(stats.linregress(l,l))

print('\nINFERENTIAL')
print('ttest_1samp:')
print(stats.ttest_1samp(l,12))

Example #11

File: testpairedstats.py Project: bennn/retic_performance

print('\n\nRelated Samples t-test')

before = list(map(float,[11,16,20,17,10]))
after = list(map(float,[8,11,15,11,11]))
print('\n\nSHOULD BE t=+2.88, 0.01<p<0.05 (df=4) ... Basic Stats 1st ed, p.359')
stats.ttest_rel(before,after,1,'Before','After')


print("\n\nPearson's r")

y = list(map(float,[8,7,7,6,5,4,4,4,2,0]))
x = list(map(float,[0,0,1,1,1,2,2,3,3,4]))
print('SHOULD BE -0.94535 (N=10) ... Basic Stats 1st ed, p.190',x,y)
print(stats.pearsonr(x,y))


print("\n\nSpearman's r")

x = list(map(float,[4,1,9,8,3,5,6,2,7]))
y = list(map(float,[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',x,y)
print(stats.spearmanr(x,y))


print('\n\nPoint-Biserial r')

gender = list(map(float,[1,1,1,1,2,2,2,2,2,2]))
score  = list(map(float,[35, 38, 41, 40, 60, 65, 65, 68, 68, 64]))
print('\nSHOULD BE +0.981257 (N=10) ... Basic Stats 1st ed, p.197')