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scipy.special.erfcinvpvalues から Z スコアを計算するために使用しています。ただし、pvalues が非常に小さくなると、erfcinv予想外に大きくなります。何か案は？

例：

In [1]: import numpy as np
In [2]: from scipy.special import erfcinv
In [3]: erfcinv(2e-16) * np.sqrt(2)
Out[3]: 8.2095361516013874
In [4]: erfcinv(1e-16) * np.sqrt(2)
Out[4]: 1.7976931348623155e+308

私はscipy 0.10.1でpython 2.6.6を実行しています。

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2 に答える 2

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If you want to calculate quantiles or p-values, the you could use the stats.distributions which find in most cases the most appropriate special function, in this case I always use stats.norm.isf for the upper tail pvalue (also because I don't want to remember what erf or erfcinv is).

>>> for y in 10.**(-np.arange(5, 30, 2)):
    print y, special.erfcinv(y) * np.sqrt(2), stats.norm.isf(y/2.), -special.ndtri(y/2)


1e-05 4.41717341347 4.41717341347 4.41717341347
1e-07 5.32672388628 5.32672388638 5.32672388638
1e-09 6.10941019166 6.10941020487 6.10941020487
1e-11 6.80650247883 6.80650249074 6.80650249074
1e-13 7.4410077655 7.44090215064 7.44090215064
1e-15 8.01401594878 8.02685888253 8.02685888253
1e-17 inf 8.57394407672 8.57394407672
1e-19 inf 9.08895010083 9.08895010083
1e-21 inf 9.57690145543 9.57690145543
1e-23 inf 10.0416376122 10.0416376122
1e-25 inf 10.4861701796 10.4861701796
1e-27 inf 10.9129127092 10.9129127092
1e-29 inf 11.3238345582 11.3238345582

The floating point problems pointed out by zero323 will still show up in many other cases.

于 2013-09-13T20:06:07.780 に答える