[SciPy-User] fisherexact.py returns None

Pete Shepard peter.shepard@gmail....
Sun Dec 20 14:50:33 CST 2009


Thanks for the reply Josef,

Numpy version =1:1.3.0-3
SciPy version=0.7.0-2


Here is the code I am using, interstingly line 99 chokes in the
testFisherExact() : example, don't know if these problems are related


# -*- coding: utf-8 -*-

#!/usr/bin/python


import re
import sys, os
from scipy.stats import *
#from scipy import array,searchsorted, histogram
import re
import operator
import matplotlib
matplotlib.use('PDF')
import pylab
"

def fisherExact(c) :
            """Performs a Fisher exact test on a 2x2 contingency table in
list of lists
               format.  Returns a tuple of (odds ratio, two-tailed P-value).

               Examples:
               >>> fisherExact([[100, 2], [1000, 5]])
               (0.25, 0.13007593634330314)
               """
            oddsRatio = c[0][0] * c[1][1] / float(c[1][0] * c[0][1])
            n1 = c[0][0] + c[0][1]
            n2 = c[1][0] + c[1][1]
            n  = c[0][0] + c[1][0]

            mode = int(float((n + 1) * (n1 + 1)) / (n1 + n2 + 2))
            pExact = hypergeom.pmf(c[0][0], n1 + n2, n1, n)
            pMode = hypergeom.pmf(c[0][0], n1 + n2, n1, n)

            if c[0][0] == mode :
                    return oddsRatio, 1.0
            elif c[0][0] < mode :
                    pLower = hypergeom.cdf(c[0][0], n1 + n2, n1, n)

                    # Binary search for where to begin upper half.
                    min = mode
                    max = n
                    guess = -1
                    while min != max :
                            guess = max if (max == min + 1 and guess == min)
else \
                                            (max + min) / 2

                            pGuess = hypergeom.pmf(guess, n1 + n2, n1, n)
                            if pGuess <= pExact and hypergeom.pmf(guess - 1,
n1 + n2, n1, n) > pExact :
                                    break
                            elif pGuess < pExact :
                                    max = guess
                            else :
                                    min = guess

                    if guess == -1 and min == max :
                            guess = min

                    return oddsRatio, pLower + hypergeom.sf(guess - 1, n1 +
n2, n1, n)
            else :
                    pUpper = hypergeom.sf(c[0][0] - 1, n1 + n2, n1, n);

                    # Special case to prevent binary search from getting
stuck.
                    if hypergeom.pmf(0, n1 + n2, n1, n) > pExact :
                            return oddsRatio, pUpper

                    # Binary search for where to begin lower half.
                    min = 0
                    max = mode
                    guess = -1
                    while min != max :
                            guess = max if (max == min + 1 and guess == min)
else \
                                            (max + min) / 2
                            pGuess = hypergeom.pmf(guess, n1 + n2, n1, n);

                            if pGuess <= pExact and hypergeom.pmf(guess + 1,
n1 + n2, n1, n) > pExact :
                                    break;
                            elif pGuess <= pExact  :
                                    min = guess
                            else :
                                    max = guess

                    if guess == -1 and min == max :
                            guess = min
                        return oddsRatio, pUpper + hypergeom.cdf(guess, n1 +
n2, n1, n)

def testFisherExact() :
    """Just some tests to show that fisherExact() works correctly."""
    def approxEqual(n1, n2) :
        return abs(n1 - n2) < 0.01

    res = fisherExact([[100, 2], [1000, 5]])
    assert(approxEqual(res[1], 0.1301))
    assert(approxEqual(res[0], 0.25))
    res = fisherExact([[2, 7], [8, 2]])
    assert(approxEqual(res[1], 0.0230141))
    assert(approxEqual(res[0], 4.0 / 56))
    res = fisherExact([[100, 2], [1000, 5]])
    #assert(approxEqual(res[1],  0.1973244))
        res = fisherExact([[5, 15], [20, 20]])
    assert(approxEqual(res[1], 0.0958044))
    res = fisherExact([[5, 16], [20, 25]])
    assert(approxEqual(res[1], 0.1725862))
    res = fisherExact([[10, 5], [10, 1]])
    assert(approxEqual(res[1], 0.1973244))

D=[[6, 1], [1, 6]]
testFisherExact()
p=fisherExact(D)
print p
On Sun, Dec 20, 2009 at 10:37 AM, <josef.pktd@gmail.com> wrote:

> On Sun, Dec 20, 2009 at 12:26 PM, Pete Shepard <peter.shepard@gmail.com>
> wrote:
> > Hello,
> >
> > I am using a fisher exact test I got from
> > http://projects.scipy.org/scipy/attachment/ticket/956/fisher.py. This
> > program takes in two tuples and returns an odds ratio and a  p-value.
> Most
> > tuples are handled nicely by the script but certain tuples eg "[[6, 1],
> [1,
> > 6]]" return "None". I am wondering if anyone knows why this is true?
>
> I get this, with and without the patch of thomas in the comments
>
> >>> fisherExact([[6, 1], [1, 6]])
> (36.0, 0.029137529137528768)
>
> Do you have an exact example? Which version of numpy and scipy?
> Looking at the code, I don't see any reason why it should return None.
>
> Josef
> >
> > TIA
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User@scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> >
> _______________________________________________
> SciPy-User mailing list
> SciPy-User@scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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