R help - Aug 2016 - glm.fit: fitted probabilities numerically 0 or 1 occurred & glm.fit: algorithm did not converge

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

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

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]]