[R-sig-ME] [R] error message and convergence issues in fitting glmer in package lme4

[Douglas Bates]

On Thu, Feb 26, 2009 at 10:58 AM, Tanja Srebotnjak
<tanjas at u.washington.edu> wrote:
> I'm resending this message because I did not include a subject line in my first posting.

Also, it is generally more effective to send questions about
lmer/glmer to the R-SIG-Mixed-Models list, which I am cc:ing on this
reply.

>> Hello,

>> I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when expanding the number of covariates I usually encounter the following error message.

>> gm8 <-
>> glmer(DIAB05F~AGE+as.factor(SEX)+poolt+poolx+poverty+fastfood+(1|as.factor(diab$fips)), family = binomial(link="logit"), data = diab, doFit=TRUE)

Error in validObject(.Object) :   invalid class "mer" object: Slot Zt
must by dims['q']  by dims['n']*dims['s']

Getting that error message from this model is peculiar.  I couldn't
actually say what might be happening without trying the fit myself.  I
would suggest setting doFit = FALSE but I think that this error would
be encountered even with doFit = FALSE.  Again, it would be hard to
say exactly what is happening here.

>> In the above, the response is person-level diabetes status as a function of AGE=age, SEX=sex, poolt=average county diabetes prevalence for previous years, poolx=pooled county diabetes prevalence for counties with similar age, sex, race, and income structure, poverty=county poverty rate, fastfood=number of fastfood places per 100,000 people in the county, and a county random effect.

>> If I leave out fastfood, the model gets at least fitted - although it doesn't converge (yet):

The version of lmer currently under development tries to address that
problem.  The optimization of the parameter estimates is performed in
a slightly different way that will, I hope, provide smoother
convergence.  If your data are not restricted and you would be willing
to send me a copy of the diab data frame I could check what happens on
that version (or you could install the development version yourself
but that is a non-trivial undertaking).  If you can send the data the
best way to send it is to create an R data file as

save(diab, file = "diab.rda")

and send the file diab.rda

>> Warning message:
>> In mer_finalize(ans) : false convergence (8)

Frequently that is a sign of an overspecified model.

>>
>
>> I would be grateful for any advice on what the problem could be and how to resolve it.
>
>>
>
>> Thanks,
>
>> Tanja
>
>
> Tanja Srebotnjak, PhD, MSc, Dipl. Stat.
> Postgraduate Fellow
> Institute for Health Metrics and Evaluation
> University of Washington
> 2301 5th Ave, Suite 600
> Seattle, WA 98121
> Email: tanjas at u.washington.edu<mailto:tanjas at u.washington.edu>
> Tel: +1-206-897-2866
> www.healthmetricsandevaluation.org<http://www.healthmetricsandevaluation.org>
>
> From: Tanja Srebotnjak
> Sent: Thursday, February 26, 2009 12:17 AM
> To: 'r-help at r-project.org'
> Subject:
>
> Hello,
>
> I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when expanding the number of covariates I usually encounter the following error message.
>
> gm8 <- glmer(DIAB05F~AGE+as.factor(SEX)+poolt+poolx+poverty+fastfood+(1|as.factor(diab$fips)), family = binomial(link="logit"), data = diab, doFit=TRUE)
> Error in validObject(.Object) :
>  invalid class "mer" object: Slot Zt must by dims['q']  by dims['n']*dims['s']
>
> In the above, the response is person-level diabetes status as a function of AGE=age, SEX=sex, poolt=average county diabetes prevalence for previous years, poolx=pooled county diabetes prevalence for counties with similar age, sex, race, and income structure, poverty=county poverty rate, fastfood=number of fastfood places per 100,000 people in the county, and a county random effect.
>
> If I leave out fastfood, the model gets at least fitted - although it doesn't converge (yet):
>
> Warning message:
> In mer_finalize(ans) : false convergence (8)
>
> I would be grateful for any advice on what the problem could be and how to resolve it.
>
> Thanks,
> Tanja
>
>
>        [[alternative HTML version deleted]]
>
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