Hello:
Please help me through my confusion. I am having trouble reconciling the
difference between what I believe is the conventional definition of an
odds ratio for a 2-by-2 table and the output produced by fisher.test()
in R. Consider the following example:
> Discrim <- matrix(c(1,10,24,17),
+ nr = 2,
+ dimnames = list(AGE = c('young', 'old'),
+ EMPLOY = c('fired', 'kept')))
> Discrim
EMPLOY
AGE fired kept
young 1 24
old 10 17
The conventional odds ratio is computed as
> (1 * 17) / (24 * 10)
[1] 0.07083333
Why is it, when I use fisher.test(), I get an estimated odds ratio like
that reported below? There, the difference seems slight, but with other
cases it can be quite large.
> fisher.test(Discrim, alternative = 'two.sided')
Fisher's Exact Test for Count Data
data: Discrim
p-value = 0.005242
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.001573963 0.606416320
sample estimates:
odds ratio
0.07407528
Thanks,
ANDREW
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