Dear Wen, Since each worker only works on one machine, your model fm2 does not make sense. Your random effects tries to model how the effect of each worker differs between machines. But you don't have that kind of information if a work only works one machine.
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 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 Wen Huang Verzonden: zondag 6 september 2009 17:49 Aan: r-help@r-project.org Onderwerp: [R] linear mixed model question Hello, I wanted to fit a linear mixed model to a data that is similar in terms of design to the 'Machines' data in 'nlme' package except that each worker (with triplicates) only operates one machine. I created a subset of observations from 'Machines' data such that it looks the same as the data I wanted to fit the model with (see code below). I fitted a model in which 'Machine' was a fixed effect and 'Worker' was random (intercept), which ran perfectly. Then I decided to complicate the model a little bit by fitting 'Worker' within 'Machine', which was saying variation among workers was nested within each machine. The model could be fitted by 'lme', but when I tried to get confidence intervals by 'intervals(fm2)' it gave me an error: Error in intervals.lme(fm2) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate variance-covariance I am wondering if this is because it is impossible to fit a model like 'fm2' or there is some other reasons? Thanks a lot! Wen ################# library(nlme) data(Machines) new.data = Machines[c(1:6, 25:30, 49:54), ] fm1 = lme(score ~ Machine, random = ~1|Worker, data = new.data) fm1 fm2 = lme(score ~ Machine, random = ~Machine-1|Worker, data = new.data) fm2 intervals(fm2) ______________________________________________ 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. Druk dit bericht a.u.b. niet onnodig af. Please do not print this message unnecessarily. 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.
