here is one approach:
res <- cbind( c(10, 5, 1, 12, 3, 8, 7, 2, 10, 1),
c(90,15,79,38,7,92,13,78,40,9) )
line <- gl(5,1,length=10, labels=LETTERS[1:5])
qa <- gl(2,5)
fit <- glm( res ~ line*qa, family=binomial )
summary(fit)
anova(fit, test='Chisq')
The interaction terms measure the difference between the different combinations
of QA method and production line, if they are all 0, then that means the effect
of QA is the same accross production lines and the qa main effect measures the
difference between the 2 methods (allowing for differences in the prodoction
lines), testing if that equals 0 should answer your question.
Hope this helps,
________________________________________
From: [EMAIL PROTECTED] [EMAIL PROTECTED] On Behalf Of Ivan Adzhubey [EMAIL
PROTECTED]
Sent: Monday, June 09, 2008 4:28 PM
To: r-help@r-project.org
Subject: [R] Comparing two groups of proportions
Hi,
I have a seemingly common problem but I can't find a proper way to approach
it. Let's say we have 5 samples (different size) of IC circuits coming from 5
production lines (A, B, C, D, E). We apply two different non-destructive QA
procedures to each sample, producing to sets of binary outcomes (passed:
no/yes). So, we have two groups of proportions:
QA1 QA2
no/yes no/yes
A 10/90 8/92
B 5/15 7/13
C 1/79 2/78
D 12/38 10/40
E 3/7 1/9
How would I test if the two QA procedures in question give significantly
different results, at the same time controlling for the possible production
line contribution? It looks like there are many variants of multiple
proportions tests available in R and various extra packages but none seems to
exactly fit this very simple problem. I would appreciate any advice.
Thanks,
Ivan
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