[SciPy-User] Why stats.fisher_exact gives results different from R?

[Stuart Reynolds]

Also see:
    https://github.com/brentp/fishers_exact_test/issues/18
.... it's a **much** faster implementation of fisher's exact, and also
differs from scipy.stats.fisher_exact.
I think the brentp implementation is the same as R's -- actually would
be good to test this and update the issue.

- Stuart


On Sat, Apr 6, 2019 at 6:22 AM <josef.pktd at gmail.com> wrote:
>
> On Sat, Apr 6, 2019 at 9:02 AM Peng Yu <pengyu.ut at gmail.com> wrote:
> >
> > See below for the output of python and R.
> >
> > Why there is a difference in the odds ratio? Given that R is most
> > widely used by statisticians, I'd prefer the python version print the
> > same results. Also, confidence intervals are missing in the python's
> > output.
> >
> > >>> stats.fisher_exact([[8, 2], [1, 5]])
> > (20.0, 0.03496503496503495)
> >
> > R> fisher.test(rbind(c(8, 2), c(1,5)))
> >
> >     Fisher's Exact Test for Count Data
> >
> > data:  rbind(c(8, 2), c(1, 5))
> > p-value = 0.03497
> > alternative hypothesis: true odds ratio is not equal to 1
> > 95 percent confidence interval:
> >     1.008849 1049.791446
> > sample estimates:
> > odds ratio
> >   15.46969
>
> estimate
> an estimate of the odds ratio. Note that the conditional Maximum
> Likelihood Estimate (MLE) rather than the unconditional MLE (the
> sample odds ratio) is used. Only present in the 2 by 2 case.
> https://stat.ethz.ch/R-manual/R-devel/library/stats/html/fisher.test.html
>
> scipy shows sample odds ratio
>
> The R help page does not show which confidence interval method they
> use, and I don't remember or never knew.
>
> Josef
>
> >
> > --
> > Regards,
> > Peng
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