[R] (no subject)
Thierry.ONKELINX at inbo.be
Thu Feb 26 11:53:29 CET 2009
R-Sig-Mixed-models is a better list for questions about lme4 and nlme.
There you are much more likely to get an answer from the mixed models
First of all I would recommend you to write the random effect as
(1|fips) instead of (1|as.factor(diab$fips)). You will run into troubles
when you change the dataset as only the random effect explicitly refers
to the dataset.
I can think of two things that may cause the errors: a lack of data
points or an overspecified model. If you have a lot of data points then
you should have a look at the correlations between the covariates.
Highly correlated covariates can lead to unstable models with false
convergences as a result.
PS An informative subject line is recommended by the posting guide.
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
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say what the experiment died of.
~ Sir Ronald Aylmer Fisher
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ensure that a reasonable answer can be extracted from a given body of
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Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Tanja Srebotnjak
Verzonden: donderdag 26 februari 2009 9:17
Aan: r-help at r-project.org
Onderwerp: [R] (no subject)
I'm trying to fit a generalized linear mixed model to estimate diabetes
prevalence at US county level. To do this I'm using the glmer() function
in package lme4. I can fit relatively simple models (i.e. few
covariates) but when expanding the number of covariates I usually
encounter the following error message.
or(diab$fips)), family = binomial(link="logit"), data = diab,
Error in validObject(.Object) :
invalid class "mer" object: Slot Zt must by dims['q'] by
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):
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.
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