On 24 Nov 2000 04:39:38 -0800, [EMAIL PROTECTED] (Konrad
Halupka) wrote:
> I have several variables (X1, X2...) measuring various traits of
> individuals and one variable (Y) which is binary (survived/did not
> survive). I would like to check if the variation in survival can be
> explained with Xi variables.
>
> It looks like a typical logistic regression problem. However it bothers
> me that the Y variable has a non-random error. The group "survived"
> surely consists of individuals who *did survived*, but the group "did
> not survive" is likely to include some individuals which actually
> survived but were not detected by an observer. How to proceed with the
> analysis?
How to proceed? -- just, proceed. Do you have any choice?
Do you have some data in hand that you have not mentioned?
There is no useful way to weight the data, if that is what you are
wondering: a regression on a dummy-variable scored 0/1 gives you
the same test as if it were scored as an (equivocating) 0/ .5. And
the coefficients are easier to understand in the first one.
When you describe your prediction equation, you might want to use a
score other than the computer-program's default cutoff to describe the
fit of prediction and outcome. But that is often the case.
Do you have a hint about who covertly survived
(which might suggest using a 3-group classification)?
Do you have an extremely high rate of success, so that
someone might be relying on the accuracy of your predictions
for some purpose?
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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