Paul -
This situation seems like an obvious candidate for a log-linear model.
See the book MASS for details. They're beyond the scope of this list.
Or try help.search("log-linear").
(and ... can you find a way to break lines when sending your email ?)
- tom blackwell - u michigan medical school - ann arbor -
On Tue, 11 Nov 2003, Paul Sorenson wrote:
> I want to investigate possible relationships between two discrete variables. I have
> tried a few things but figured you guys might be able to point me at some purpose
> built functions.
>
> Our scientists score results of tests which are performed in lets say, 8 positions.
> The scores are assigned a value of 1,2,3 or 4. I want to know if there is a
> correlation between the test results and the position. The scientists have a
> feeling that position 1 does not score as high as the others.
>
> Not all 8 positions are always used, so the frequency of all test results can be
> substantially biased towards the first position. Here is an example dataset (not
> very biased) resulting from table(result, position):
>
> 1 2 3 4 5 6 7 8
> 0 3 3 2 2 0 3 3 0
> 1 11 4 6 7 7 3 3 5
> 2 38 37 32 38 31 21 23 27
> 3 51 66 54 66 57 37 58 56
> 4 3 1 3 0 1 0 1 1
>
> Because the test results are highly quantized, the boxplots I tried all looked
> pretty much the same.
>
> The bias means that stacked barplots aren't that useful for visualising the data.
> With a bit of data processing I guess I could normalise the total frequencies of
> each test position.
>
> I also tried a correlation between the two variables. The answer is non-zero but I
> am not sure that any relationship between the two variables would be monotonic (BTW
> cor() give me the correlation coefficient, how do I get the "confidence" of the
> coefficient?)
>
> Maybe I am overlooking the obvious, like just averaging the scores.
>
> cheers
>
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