假设我有一个F值以及相关的自由度df1和df2。如何使用python以编程方式计算与这些数字关联的p值?

注意:我不接受使用scipy或statsmodels的解决方案。

最佳答案

可以使用正则化(不完整)的beta函数I(x; a, b)来计算F分布的CDF(以及由此得出的p值),例如参见MathWorld。使用此blogI(x; a, b)的代码(仅使用math),则p值为

1 - incompbeta(.5*df1, .5*df2, float(df1)*F/(df1*F+df2))

这里是一些匹配scipy.stats.f.sf的样本值的结果:
In [57]: F, df1, df2 = 5, 20, 18

In [58]: 1 - incompbeta(.5*df1, .5*df2, float(df1)*F/(df1*F+df2))
Out[58]: 0.0005812207389501722

In [59]: st.f.sf(F, df1, df2)
Out[59]: 0.00058122073922042188

以防万一博客消失了,下面的代码:
import math

def incompbeta(a, b, x):

    ''' incompbeta(a,b,x) evaluates incomplete beta function, here a, b > 0 and 0 <= x <= 1. This function requires contfractbeta(a,b,x, ITMAX = 200)
    (Code translated from: Numerical Recipes in C.)'''

    if (x == 0):
        return 0;
    elif (x == 1):
        return 1;
    else:
        lbeta = math.lgamma(a+b) - math.lgamma(a) - math.lgamma(b) + a * math.log(x) + b * math.log(1-x)
        if (x < (a+1) / (a+b+2)):
            return math.exp(lbeta) * contfractbeta(a, b, x) / a;
        else:
            return 1 - math.exp(lbeta) * contfractbeta(b, a, 1-x) / b;

def contfractbeta(a,b,x, ITMAX = 200):

    """ contfractbeta() evaluates the continued fraction form of the incomplete Beta function; incompbeta().
    (Code translated from: Numerical Recipes in C.)"""

    EPS = 3.0e-7
    bm = az = am = 1.0
    qab = a+b
    qap = a+1.0
    qam = a-1.0
    bz = 1.0-qab*x/qap

    for i in range(ITMAX+1):
        em = float(i+1)
        tem = em + em
        d = em*(b-em)*x/((qam+tem)*(a+tem))
        ap = az + d*am
        bp = bz+d*bm
        d = -(a+em)*(qab+em)*x/((qap+tem)*(a+tem))
        app = ap+d*az
        bpp = bp+d*bz
        aold = az
        am = ap/bpp
        bm = bp/bpp
        az = app/bpp
        bz = 1.0
        if (abs(az-aold)<(EPS*abs(az))):
            return az

    print 'a or b too large or given ITMAX too small for computing incomplete beta function.'

关于python - 计算python中的F分布p值?,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/38113120/

10-12 22:44