[R] glm's for a logistic regression - no warnings?

[Xochitl CORMON]

Hi,

I did have warning messages about convergence issues using binomial GLM 
with logit link with my data in the past....

Do you detect separation using the function separation.detection{brglm}?

Regards,

Xochitl C.


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Xochitl CORMON
+33 (0)3 21 99 56 84

Doctorante en sciences halieutiques
PhD student in fishery sciences

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IFREMER
Centre Manche Mer du Nord
150 quai Gambetta
62200 Boulogne-sur-Mer

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Le 01/10/2013 16:41, Dimitri Liakhovitski a écrit :
> I have this weird data set with 2 predictors and one dependent variable -
> attached.
>
> predictor1 has all zeros except for one 1.
> I am runnning a simple logistic regression:
>
> temp<-read.csv("x data for reg224.csv")
> myreg<- glm(dv~predictor1+predictor2,data=temp,
>               family=binomial("logit"))
> myreg$coef2
>
> Everything runs fine and I get the coefficients - and the fact that there
> is only one 1 on one of the predictors doesn't seem to cause any problems.
>
> However, when I run the same regression in SAS, I get warnings:
>   Model Convergence Status  Quasi-complete separation of data points
> detected.
>
> Warning: The maximum likelihood estimate may not exist.
> Warning: The LOGISTIC procedure continues in spite of the above warning.
> Results shown are based on the last maximum likelihood iteration. Validity
> of the model fit is questionable.
>
> And the coefficients SAS produces are quite different from mine.
>
> I know I'll probably get screamed at because it's not a pure R question -
> but any idea why R is not giving me any warnings in such a situation?
> Does it have no problems with ML estimation in this case?
>
> Thanks a lot!
>
>
>
>
>
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