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

[Shivi Bhatia]

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]]
>>
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>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>

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