[R] glm.fit: fitted probabilities numerically 0 or 1 occurred & glm.fit: algorithm did not converge

[Shivi Bhatia]

Sure Burt, i will share the data after masking it.  it isn't big

regards, Shivi

On Fri, Aug 12, 2016 at 8:36 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote:

> 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).
>
> Cheers,
> Bert
> Bert Gunter
>
> "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>
> > wrote:
> >
> >> Dear Shivi
> >>
> >> Can you show us the output?
> >>
> >> And please do not post in HTML as it will mangle your post into
> >> unreadability.
> >>
> >> 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
> >>> a
> >>>
> >>> 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
> >>> shows
> >>> 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 =
> >>> binomial)
> >>>
> >>> 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
> >>> algorithm
> >>> hit the maximum number of allowed iterations before signalling
> >>> convergence.
> >>> The default,
> >>> documented in ?glm.control is 25."
> >>>
> >>> Kindly suggest on the above case and if i have to change my outcome
> var as
> >>> as.factor.
> >>>
> >>> Thank you, Shivi
> >>>
> >>>         [[alternative HTML version deleted]]
> >>>
> >>> ______________________________________________
> >>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >>> https://stat.ethz.ch/mailman/listinfo/r-help
> >>> PLEASE do read the posting guide http://www.R-project.org/posti
> >>> ng-guide.html
> >>> and provide commented, minimal, self-contained, reproducible code.
> >>>
> >>>
> >> --
> >> Michael
> >> http://www.dewey.myzen.co.uk/home.html
> >>
> >
> >         [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > 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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