[R] Is it safe? Cochran etc
Dan Bolser
dmb at mrc-dunn.cam.ac.uk
Sun Oct 10 13:03:13 CEST 2004
Hey
On Sat, 9 Oct 2004, Frederico Zanqueta Poleto wrote:
>Dan,
>
>I don't know what is the theory behind this "hybrid" option and what
>consists the Cochran conditions.
>
>However, I think even if you suppose the asymptotic distribution is not
>too accurate, because your sampled 1, there is a too strong association
>of A and B, as this can be noticed by conservative methods such as using
>the Yates continuity correction or Wald/Neyman tests (that usually does
>not reject the null hypothesis of no interaction much more than the
>Pearson/score test and likelihood ratio test, in this order) of the log
>odds.
So I read from this that Wald/Neyman tests of the log odds is
conservative, but not by much?
>Both procedures inflate the pvalues, but not sufficiently to change your
>conclusion as you can notice by:
>
>> chisq.test(dat,correct=FALSE)
>
> Pearson's Chi-squared test
>
>data: dat
>
>X-squared = 6.0115, df = 1, p-value = 0.01421
>
>> chisq.test(dat)
>
> Pearson's Chi-squared test with Yates' continuity correction
>
>data: dat
>
>X-squared = 5.1584, df = 1, p-value = 0.02313
OK, I see the p-value was inflated, making Yates more conservative.
>> 1-pchisq( (log(878702/(13714*506))^2)/(1+1/878702+1/13714+1/506) ,1)
># Wald test of null log odds
>
>[1] 0.03898049
OK...
logOD <- log((1*878702)/(13714*506))
same as
logOD <- log( ( 1/ 506) /
(13714/878702)
)
The odds ratio has an approximatly normal distribution
Then you divide the logOD^2 by the variance (same as
logOD/StDev?)... Nope...
Is the above just the nature of the test?
Using data from the paper on the description of the log odds I have...
dat <- matrix(c(141,928,420,13525),nr=2)
dat
1-pchisq((log((141*13525)/(928*420))^2)/(1/141+1/13525+1/928+1/420),1)
[1] 0
a very cautious ... ok....
The data does not have a logOD of 1, very strongly, so the p-value is 0
for that test.
How does the above differ from just saying ...
chisq.test(dat)
Other than the latter appears to never go below 2.2e-16 and the former
hapily says 0.
>The book "Categorical data analysis" from Agresti (2002) has an ample
>discussion about tests like this on chapters 1 (basics and one sample)
>and 3 (two variables). You may look there if you still have doubts about
>this tests.
I should have got that book last week when I was still enthusiastic about
this problem.
Thanks very much for your help,
Dan.
>Sincerely,
>
>
More information about the R-help
mailing list