[R-sig-ME] Fwd: glm.nb convergence issues

[Matthew Boden]

Thank you, Thierry.  A csv file is attached; hopefully, that works.

The numerator divided by the denominator will always fall between 0 and 1
for these data, and is typically very close to 1.
The predictor variable ranges from about 3 to 18, but even when scaled, the
model won't converge.

Matt












On Wed, Mar 20, 2019 at 1:48 AM Thierry Onkelinx <thierry.onkelinx using inbo.be>
wrote:

> Dear Matthew,
>
> The mailing list accepts only a limited number of file formats as
> attachment. Your data got stripped. Can you resend the data or send a link
> to the data?
>
> The offset requires the log because the model is using the log link. Your
> model fits log(E(wait_n)) = offset(log(wait_d)) + covariates which you can
> rewrite as log(E(wait_n)) - offset(log(wait_d)) = covariates or
> log(E(wait_n) / wait_d) = covariates. Pick a relevant magnitude of wait_d
> so that the magnitude of wait_n/wait_d is somewhat near 1. E.g. we don't
> express the number of inhabitats per m² but rather per km²
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be
>
>
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>
>
> Op wo 20 mrt. 2019 om 06:43 schreef Matthew Boden <
> matthew.t.boden using gmail.com>:
>
>> Hello,
>>
>>
>>
>> I’m working my way through convergence issues related to a negative
>> binomial mixed model with an offset and random intercept. Data attached.
>>
>>
>>
>> A3 <- glmer.nb(wait_n ~ offset(log(wait_d)) +  scale(spr) + (1 | check),
>> data = ST)
>>
>> summary(A3)
>>
>>
>>
>> #Model failed to converge with max|grad| = 0.0031459 (tol = 0.001,
>> component 1)
>>
>> #Model is nearly unidentifiable: very large eigenvalue
>>
>> # - Rescale variables?
>>
>>
>>
>> I have two questions.
>>
>>
>>
>> 1) Outcome variable (wait_n) values are much larger than predictor (spr)
>> values.
>>
>>
>>
>> Mean wait_n = 11,783
>>
>> Mean spr = 7.34
>>
>>
>>
>> Would transforming the predictor (e.g., multiply by 1000) and/or offset
>> make sense here. The offset confuses the issue (or, me) - as a log, it is
>> quite small relative to the outcome and on but on the same scale as the
>> other predictor.
>>
>>
>>
>> 2) The use of allFit to try different optimizers seems to be giving
>> conflicting results.
>>
>>
>>
>> A3.all <- allFit(A3, meth.tab = NULL)
>>
>> ss <- summary(A3.all)
>>
>>
>>
>> #bobyqa : [OK]
>>
>> #Nelder_Mead : [OK]
>>
>> #nlminbwrap : [OK]
>>
>> #nmkbw : [OK]
>>
>> #optimx.L-BFGS-B : [OK]
>>
>> #nloptwrap.NLOPT_LN_NELDERMEAD : [OK]
>>
>> #nloptwrap.NLOPT_LN_BOBYQA : [OK]
>>
>>
>>
>> ss$ which.OK
>>
>>
>>
>> #bobyqa                         TRUE
>>
>> #Nelder_Mead                    TRUE
>>
>> #nlminbwrap                     TRUE
>>
>> #nmkbw                          TRUE
>>
>> #optimx.L-BFGS-B                TRUE
>>
>> #nloptwrap.NLOPT_LN_NELDERMEAD  TRUE
>>
>> #nloptwrap.NLOPT_LN_BOBYQA      TRUE
>>
>>
>>
>> What do you know, all optimizers supposedly work.
>>
>> However, some of them lead to a model that converges, and others don’t.
>>
>>
>>
>> A3a <- glmer.nb(wait_n ~ offset(log(wait_d)) +  scale(spr) + (1 | check),
>> data = ST, glmerControl(optimizer = "bobyqa"))
>>
>>
>>
>> #Model is nearly unidentifiable: very large eigenvalue
>>
>> # - Rescale variables?
>>
>>
>>
>> A3b <- glmer.nb(wait_n ~ offset(log(wait_d)) +  scale(spr) + (1 | check),
>> data = ST, glmerControl(optimizer = "Nelder_Mead"))
>>
>>
>>
>> #Model failed to converge with max|grad| = 0.00553513 (tol = 0.001,
>> component 1)
>>
>> #Model is nearly unidentifiable: very large eigenvalue
>>
>> # - Rescale variables?
>>
>>
>>
>> A3c <- glmer.nb(wait_n ~ offset(log(wait_d)) +  scale(spr) + (1 | check),
>> data = ST, glmerControl(optimizer = "nlminbwrap"))
>>
>>
>>
>> #Model is nearly unidentifiable: very large eigenvalue
>>
>> # - Rescale variables?
>>
>>
>>
>> A3d <- glmer.nb(wait_n ~ offset(log(wait_d)) +  scale(spr) + (1 | check),
>> data = ST, glmerControl(optimizer = "nmkbw"))
>>
>>
>>
>> #Model failed to converge with max|grad| = 0.00177926 (tol = 0.001,
>> component 1)
>>
>> #Model is nearly unidentifiable: very large eigenvalue
>>
>> # - Rescale variables?
>>
>>
>>
>> I’m trying to understand why allFit responds that all optimizers converge
>> when they actually do not.
>>
>>
>>
>> Thank you,
>>
>> Matt
>>
>>
>>
>> Matthew Boden, Ph.D.
>>
>> Senior Evaluator
>>
>> Program Evaluation & Resource Center
>>
>> Office of Mental Health & Suicide Prevention
>>
>> Veterans Health Administration
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>>
>