Rich Ulrich wrote:
>
> 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?
>
Thanks for response. Indeed, I was concerned if there were some methods
of weighting the data.
Birds are marked and after 12 months the observer attempts to find them
again in the field. Of course it is impossible to search a very wide
area. Those individuals who "covertly survive" have a tendency to
disperse farther than those who survive "overtly" (i.e. can be relocated
within a reasonable distance from the place where they were originally
found).
Regards,
k
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