Dear Tanja, 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 specialists.
First of all I would recommend you to write the random effect as (1|fips) instead of (1|as.factor(diab$fips)). You will run into troubles when you change the dataset as only the random effect explicitly refers to the dataset. I can think of two things that may cause the errors: a lack of data points or an overspecified model. If you have a lot of data points then you should have a look at the correlations between the covariates. Highly correlated covariates can lead to unstable models with false convergences as a result. HTH, Thierry PS An informative subject line is recommended by the posting guide. ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 thierry.onkel...@inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Namens Tanja Srebotnjak Verzonden: donderdag 26 februari 2009 9:17 Aan: r-help@r-project.org Onderwerp: [R] (no subject) Hello, I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when expanding the number of covariates I usually encounter the following error message. gm8 <- glmer(DIAB05F~AGE+as.factor(SEX)+poolt+poolx+poverty+fastfood+(1|as.fact or(diab$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]] ______________________________________________ R-help@r-project.org mailing list 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. Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document. The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document. ______________________________________________ R-help@r-project.org mailing list 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.
