You should look up the Hauck-Donne phenomenon, which shows that
with binomial GLMs, the standard error can grow faster than
the effect size. Complete separation results, for example,
when one predictor (or a combination of several predictors)
perfectly predicts the response. Something like this
Thanks Ken for your reply. No doubt your english is quite tough!! I
understand something is not normal with the 5th explanatory variable
(se:2872.17069!) However could not understand what you mean by "You
seem to be getting complete separation on X5 "?
Can you please be more elaborate?
Thanks,
O
Christofer Bogaso gmail.com> writes:
> Dear all, I am fitting a LOGIT model on this Data...
<< snip >>---
> glm(Data[,1] ~ Data[,-1], binomial(link = logit))
>
> Call: glm(formula = Data[, 1] ~ Data[, -1], family = binomial(link = logit))
>
> Coefficients:
> (Intercept) Data[, -
Dear all, I am fitting a LOGIT model on this Data...
Data <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
0, 1, 1, 0, 1, 0, 47, 58, 82, 100, 222, 164, 161, 70, 219, 81,
209, 182, 185, 104, 126, 192, 95, 245, 9
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