Why stats.fisher_exact gives results different from R?
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 -- Regards, Peng
On Sat, Apr 6, 2019 at 9:02 AM Peng Yu <pengyu.ut@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 _______________________________________________ SciPy-User mailing list SciPy-User@python.org https://mail.python.org/mailman/listinfo/scipy-user
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@gmail.com> wrote:
On Sat, Apr 6, 2019 at 9:02 AM Peng Yu <pengyu.ut@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 _______________________________________________ SciPy-User mailing list SciPy-User@python.org https://mail.python.org/mailman/listinfo/scipy-user
_______________________________________________ SciPy-User mailing list SciPy-User@python.org https://mail.python.org/mailman/listinfo/scipy-user
Also: BUG: stats: fisher_exact returns incorrect p-value https://github.com/scipy/scipy/issues/4130 - Stu On Sat, Apr 6, 2019 at 12:37 PM Stuart Reynolds <stuart@stuartreynolds.net> wrote:
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@gmail.com> wrote:
On Sat, Apr 6, 2019 at 9:02 AM Peng Yu <pengyu.ut@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 _______________________________________________ SciPy-User mailing list SciPy-User@python.org https://mail.python.org/mailman/listinfo/scipy-user
_______________________________________________ SciPy-User mailing list SciPy-User@python.org https://mail.python.org/mailman/listinfo/scipy-user
But I don't see the computation of the odds ratio. On 4/6/19, Stuart Reynolds <stuart@stuartreynolds.net> wrote:
Also:
BUG: stats: fisher_exact returns incorrect p-value https://github.com/scipy/scipy/issues/4130
- Stu
-- Regards, Peng
participants (3)
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josef.pktd＠gmail.com -
Peng Yu -
Stuart Reynolds