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RE: st: Binary and ologit

From   "Sarah Edgington" <[email protected]>
To   <[email protected]>
Subject   RE: st: Binary and ologit
Date   Tue, 31 Jul 2012 17:56:05 -0700

This is one of those cases where you might get clearer answers if you make
it clear what your actual research question is.  In general, what modeling
approach you use depends a great deal on what you're trying to figure out.
It sounds to me like you're proposing an outcome for which there is no
variation for the cattle in your sample.  So even if you do code diagnosis
as "acute", "chronic", and "diseased" you would be able to perfectly predict
whether an animal fell into the category "diseased" by knowing whether it
was a cow or sheep.  Right?

For the sheep you can model whether they fall into the acute or chronic
categories but I don't understand what question you're trying to answer with
a model that includes both animals.


-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Shittu, Aminu
Sent: Tuesday, July 31, 2012 5:37 PM
To: [email protected]
Subject: Re: st: Binary and ologit

 Hi Maarten,

Apologies my initial posting might be vague. I am suppose to add that none
of the over 12,000 animals in my 30 years dataset is healthy. We selected
animals that were all diagnosed with our particular disease of interest,
whose 2 clinical stages (acute and chronic) were recorded in sheep, but it
only said 'diseased' in cattle - same disease though. Could this diagnosis
with different levels (acute, chronic and diseased) be treated as ordinal
even though cattle has only 1 level in the dataset, and 2 levels for sheep?


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