spearman相關(guān)系數(shù)的檢驗(yàn)？Python的公式：r,pstats.spearmanr（X1,X2）結(jié)果為:r:相關(guān)系數(shù)，p：p_value功能：是兩個(gè)數(shù)據(jù)集之間關(guān)系單調(diào)性的非參數(shù)度量，Spearm

spearman相關(guān)系數(shù)的檢驗(yàn)？

Python的公式：r,pstats.spearmanr（X1,X2）結(jié)果為:r:相關(guān)系數(shù)，p：p_value功能：是兩個(gè)數(shù)據(jù)集之間關(guān)系單調(diào)性的非參數(shù)度量，Spearman相關(guān)性不題中兩個(gè)數(shù)據(jù)集是標(biāo)準(zhǔn)正態(tài)分布的。（檢驗(yàn)2個(gè)變量之間的相關(guān)性）r:這個(gè)相關(guān)系數(shù)在-1和1之間變化，0意思是沒(méi)有相關(guān)性。相關(guān)系數(shù)的絕對(duì)值約接近1，相關(guān)性越高，p:p值查閱地來(lái)表示不查找系統(tǒng)產(chǎn)生具備Spearman相關(guān)性的數(shù)據(jù)集的概率（簡(jiǎn)單通俗的說(shuō)，那就是2個(gè)變量不相關(guān)的概率，總體上，若2個(gè)變量的相關(guān)系數(shù)越高，則P值會(huì)低些較高）。p值并不完全可靠，但相對(duì)于大于1500左右的數(shù)據(jù)集很有可能是合理不的。例子：r,pstats.spearmanr([1,2,3,4,5],[5,6,7,8,7])(1234321)x2nnp.random.randn(100,2)y2nnp.random.randn(100,2)stats.spearmanr(x2n)#最終(0.059969996999699973,0.55338590803773591)stats.spearmanr(x2n[:,0],x2n[:,1])#最終(0.059969996999699973,0.55338590803773591)rho,pvalstats.spearmanr(x2n,y2n)#最后gtgtgtrhoarray([[1.,0.05997,0.18569457,0.06258626],[0.05997,1.,0.110003,0.02534653],[0.18569457,0.110003,1.,0.03488749],[0.06258626,0.02534653,0.03488749,1.]])gtgtgtpvalarray([[0.,0.55338591,0.06435364,0.53617935],[0.55338591,0.,0.27592895,0.80234077],[0.06435364,0.27592895,0.,0.73039992],[0.53617935,0.80234077,0.73039992,0.]])gtgtgtrho,pvalstats.spearmanr(x2n.T, y2n.T,axis1)gtgtgtrhoarray([[1.,0.05997,0.18569457,0.06258626],[0.05997,1.,0.110003,0.02534653],[0.18569457,0.110003,1.,0.03488749],[0.06258626,0.02534653,0.03488749,1.]])stats.spearmanr(x2n,y2n,axisNone)#總體的相關(guān)性：(0.10816770419260482,0.1273562188027364)stats.spearmanr(x2n.ravel(),y2n.ravel())#總體的相關(guān)性：(0.10816770419260482,0.1273562188027364)xintnp.random.randint(10,size(100,2))stats.spearmanr(xint)#(0.052760927029710199,0.60213045837062351)

fab在python中的意思？

?請(qǐng)看：fabs()方法趕往數(shù)字的絕對(duì)值，如math.fabs(-10)直接返回10.0.

?語(yǔ)法：

importmath

math.fabs(x)

?參數(shù)：x-數(shù)值表達(dá)式

?返回值：趕往數(shù)字的絕對(duì)值.

?實(shí)例：

#!/usr/bin/python

#-*-coding:UTF-8-*-

importmath

#再導(dǎo)入數(shù)學(xué)模塊

print#34math.fabs(-45.17):#34,math.fabs(-45.17)

print#34math.fabs(100.12):#34,math.fabs(100.12)

print#34math.fabs(100.72):#34,math.fabs(100.72)

print#34math.fabs(119L):#34,math.fabs(119L)

print#34math.fabs(math.pi):#34,math.fabs(math.pi)

作為輸出結(jié)果：

math.fabs(-45.17):45.17

math.fabs(100.12):100.12

math.fabs(100.72):100.72

math.fabs(119L):119.0

math.fabs(math.pi):3.14159265359