[R] glm.fit: fitted probabilities numerically 0 or 1 occurred & glm.fit: algorithm did not converge
bgunter.4567 at gmail.com
Fri Aug 12 17:06:50 CEST 2016
1. No, changing to factor will make no difference.
2. I think that most likely your problem is your model is not
estimable/your design matrix is singular. You should resolve this by
consulting with a local statistical expert or, if your data set is not
too large or confidential, posting your full dataset using dput() (see
?dput for how to do this).
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Fri, Aug 12, 2016 at 7:58 AM, Shivi Bhatia <shivipmp82 at gmail.com> wrote:
> Hi Michael,
> There is no output as the model does not generate any coefficients and
> simply throws this error.
> I hope you are not asking for a reproducible example.
> On Fri, Aug 12, 2016 at 7:30 PM, Michael Dewey <lists at dewey.myzen.co.uk>
>> Dear Shivi
>> Can you show us the output?
>> And please do not post in HTML as it will mangle your post into
>> On 12/08/2016 10:10, Shivi Bhatia wrote:
>>> Hi Team,
>>> I am creating *my first* Logistic regression on R Studio. I am working on
>>> C-SAT data where rating (score) 0-8 is a dis-sat whereas 9-10 are SAT. As
>>> these were in numeric form so i had as below created 2 classes:
>>> new$survey[new$score>=0 & new$score<=8]<- 0
>>> new$survey[new$score>=9]<- 1
>>> This works fine however the class still shows as "numeric" and levels
>>> as "NULL". Do i still need to use "as.factor" to let R know these are
>>> categorical variables.
>>> Also i have used the below code to run a logistic regression with all the
>>> possible predictor variables:
>>> glm.fit= glm(survey ~ support_cat + region+ support_lvl+ skill_group+
>>> application_area+ functional_area+
>>> repS+ case_age+ case_status+ severity_level+
>>> sla_status+ delivery_segmentation, data = SFDC, family =
>>> But it throws an error:-
>>> Warning messages:
>>> 1: glm.fit: algorithm did not converge
>>> 2: glm.fit: fitted probabilities numerically 0 or 1 occurred
>>> I checked online for the error and it says:
>>> "glm() uses an iterative re-weighted least squares algorithm. The
>>> hit the maximum number of allowed iterations before signalling
>>> The default,
>>> documented in ?glm.control is 25."
>>> Kindly suggest on the above case and if i have to change my outcome var as
>>> Thank you, Shivi
>>> [[alternative HTML version deleted]]
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>>> PLEASE do read the posting guide http://www.R-project.org/posti
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> [[alternative HTML version deleted]]
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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