Thank you very much, Bert - it's very helpful.
This post says that R issues a warning:
Warning message:*glm.fit: fitted probabilities numerically 0 or 1 occurred *
However, in my case there is no warning. How could I detect complete
separation in my data? I need to be able to flag it in my function.
Thank you very much!
Dimitri
On Tue, Oct 1, 2013 at 10:52 AM, Xochitl CORMON
<xochitl.cor...@ifremer.fr>wrote:
> 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.
>
>
> <>< <>< <>< <><
>
> Xochitl CORMON
> +33 (0)3 21 99 56 84
>
> Doctorante en sciences halieutiques
> PhD student in fishery sciences
>
> <>< <>< <>< <><
>
> IFREMER
> Centre Manche Mer du Nord
> 150 quai Gambetta
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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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>>
>
> ______________________________**________________
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> and provide commented, minimal, self-contained, reproducible code.
>
--
Dimitri Liakhovitski
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