R help - Jul 2009 - Fisher's exact test

Hi,
I'm trying to run Fisher's Exact test on the data below, but I'm not
sure how interpret the data shown. Can someone tell me what this is
saying? Looking at the numbers it should be that there's no significant
difference between the HDL and LDL, but a p-value of 1 seems high. Is
the low value in the LDL unbound making the test unstable and should I
be using an alternative?

Best regards
James
> data <-
matrix(c(27,8,9,2),nr=2,dimnames=list(c("HDL","LDL"),c("Bound","Unbound")))
> data
    Bound Unbound
HDL    27       9
LDL     8       2
> fisher.test(data)
	Fisher's Exact Test for Count Data

data:  data
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.06629276 4.88625959
sample estimates:
odds ratio
 0.7545197

James Allsopp-2 wrote:
> 
> Hi,
> I'm trying to run Fisher's Exact test on the data below, but
I'm not
>     Bound Unbound
> HDL    27       9
> LDL     8       2
>> fisher.test(data)
> 
> 
At odds of 3 and 4 with one count =2, even the best test won't help.

Dieter

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On 7/17/2009 12:13 PM, James Allsopp wrote:
> Hi,
> I'm trying to run Fisher's Exact test on the data below, but
I'm not
> sure how interpret the data shown. Can someone tell me what this is
> saying? Looking at the numbers it should be that there's no significant
> difference between the HDL and LDL, but a p-value of 1 seems high. Is
> the low value in the LDL unbound making the test unstable and should I
> be using an alternative?
The p value of 1 says that the data could not be any more consistent 
with the hypothesis than they are. With the margins you have, there are 
only 11 possible outcomes, and your table is the most probable one under 
the hypothesis of independence.

You can see the probabilities of all possible outcomes using

dhyper(0:10, 11, 35, 10)

With rounding, I see

 > round(dhyper(0:10, 11, 35, 10), 2)
  [1] 0.05 0.19 0.32 0.27 0.13 0.04 0.01 0.00 0.00 0.00 0.00

where the results go from 0 to 10 in the lower right corner.  You had a 2.

Duncan Murdoch
> 
> Best regards
> James
> 
>> data <-
>
matrix(c(27,8,9,2),nr=2,dimnames=list(c("HDL","LDL"),c("Bound","Unbound")))
>> data
>     Bound Unbound
> HDL    27       9
> LDL     8       2
>> fisher.test(data)
> 
> 	Fisher's Exact Test for Count Data
> 
> data:  data
> p-value = 1
> alternative hypothesis: true odds ratio is not equal to 1
> 95 percent confidence interval:
>  0.06629276 4.88625959
> sample estimates:
> odds ratio
>  0.7545197
> 
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