# [R] Comparing two groups of proportions

Rolf Turner r.turner at auckland.ac.nz
Tue Jun 10 05:16:57 CEST 2008

```Your approach tacitly assumes --- as did the poster's question --- that
the probability of passing an item by one method is *independent* of
whether it is passed by the other method.  Which makes the methods
effectively independent of the nature of the item being assessed!

Not much actual quality being assured there!

cheers,

Rolf Turner

On 10/06/2008, at 2:57 PM, Greg Snow wrote:

> 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
>
> Hope this helps,
>
>
> ________________________________________
> From: r-help-bounces at r-project.org [r-help-bounces at r-project.org]
> Sent: Monday, June 09, 2008 4:28 PM
> To: r-help at 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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