Dear Ben, Your questions suite better in R-SIG-Mixed-Models which I am cc'ing. Have checked the mailing list? Try RSiteSearch("lme4 false convergence")
HTH, Thierry ------------------------------------------------------------------------ ---- 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 [EMAIL PROTECTED] 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: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Namens Ben Zuckerberg Verzonden: maandag 24 november 2008 18:37 Aan: r-help@r-project.org Onderwerp: [R] lme4 and false convergence Dear R-users, I am using the most updated package of lme4 (lme4_0.999375-2). I have a data set consisting of ~900 observations at ~440 independent survey sites. There are an unbalanced number of surveys at each site. I am attempting to develop several models evaluating the presence/absence of a species (PRES) at these random sites (SITE) using a number of predictor variables. The response variable (PRES) is binomial and the included predictor variables are either proportions (e.g., AG, FOR, OPEN, LDEV,or DEV) or numerical (COUNT[min. 1 to max. 37,mean = 10]). --------------------------------------------------------------------- PROBLEM 1: I have developed the following GLMM: mix.1<-glmer(PRES~AG+FOR+OPEN+LDEV+DEV+COUNT+(1|SITE),family=binomial,da ta=merge1) I receive the following error: Warning message: In mer_finalize(ans) : false convergence (8) There is model output, but I am worried it might be biased. I have tried: -Data transformation of the predictor variables (e.g., log(COUNT)), which does seem successful in some simpler models, but does not work consistently. -The most updated version of lme4 --------------------------------------------------------------------- PROBLEM #2: In addition, I am interested in visualizing the predicted probabilities from this output using the fixed effect function, but receive the following message: fixef(mix.1) Error in UseMethod("fixef") : no applicable method for "fixef" Any suggestions would be greatly appreciated! ______________________________________________ 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.