[R] Is it safe? Cochran etc

[Dan Bolser]

Why can't I just use Log odds? Does the standard error of the logs score
depend on a similar chisq assumption?



On Sat, 9 Oct 2004, Dan Bolser wrote:

>
>I have the following contingency table
>
>dat <- matrix(c(1,506,13714,878702),nr=2)
>
>And I want to test if their is an association between events 
>
>A:{a,not(a)} and B:{b,not(b)}
>
>        | b   | not(b) |
>--------+-----+--------+
> a      |   1 |  13714 |
>--------+-----+--------+
> not(a) | 506 | 878702 |
>--------+-----+--------+
>
>I am worried that prop.test and chisq.test are not valid given the low
>counts and low probabilites associated with 'sucess' in each category.
>
>Is it safe to use them, and what is the alternative? (given that
>fisher.test can't handle this data... hold the phone...
>
>I just found fisher.test can handle this data if the test is one-tailed
>and not two-tailed.
>
>I don't understand the difference between chisq.test, prop.test and
>fisher.test when the hybrid=1 option is used for the fisher.test.
>
>I was using the binomial distribution to test the 'extremity' of the
>observed data, but now I think I know why that is inapropriate, however,
>with the binomial (and its approximation) at least I know what I am
>doing. And I can do it in perl easily...
>
>Generally, how should I calculate fisher.test in perl (i.e. what are its
>principles). When is it safe to approximate fisher to chisq?
>
>I cannot get insight into this problem...
>
>How come if I do...
>
>dat <- matrix(c(50,60,100,100),nr=2)
>
>prop.test(dat)$p.value
>chisq.test(dat)$p.value
>fisher.test(dat)$p.value
>
>I get 
>
>[1] 0.5173269
>[1] 0.5173269
>[1] 0.4771358
>
>When I looked at the binomial distribution and the normal approximation
>thereof with similar counts I never had a p-value difference > 0.004
>
>I am so fed up with this problem :(
>
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