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

[Thierry Onkelinx]

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
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

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