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Xochitl CORMON
Le 01/10/2013 17:29, Dimitri Liakhovitski a écrit :
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
*
Actually the warning message should be something like:
glm.fit: algorithm did not converge
The fist warning is not fatal contrary to the second one..
(https://stat.ethz.ch/pipermail/r-help/2012-March/307352.html)
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.
As said use the separation dectection function: separation.detection{brglm}
Thank you very much!
Dimitri
On Tue, Oct 1, 2013 at 10:52 AM, Xochitl CORMON
<xochitl.cor...@ifremer.fr <mailto: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 <tel:%2B33%20%280%293%2021%2099%2056%2084>
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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--
Dimitri Liakhovitski
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