Dear Daryl, 1) use pdDiag to get indepedent random effects. lme(Y ~ disease, random = list(radiologist = pdDiag(~disease)), weight = varGroup(~disease))
2) lmer can't handle variance structures like nlme can. I believe it is on Douglas Bates to do list. But rather at the bottom of it. HTH, Thierry PS The R-SIG-Mixed models is a more appropriate list for this kind of questions. ------------------------------------------------------------------------ ---- 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 Daryl Morris Verzonden: dinsdag 30 juni 2009 8:17 Aan: r-help@r-project.org Onderwerp: [R] lmer (or lme) with heteroscedasticity Hello, I'm trying to fit a mixed-effects model with a single binary predictor (case/control status in my case), a random intercept (e.g. dependent on radiologist) and also a random slope (a per-radiologist difference between cases and controls). I know how to do that, but what I don't know how to do is both of (1) allowing the variance to be different for cases and controls (2) forcing the random effects to be independent By "both", I mean: (1) Using lme (from nlme library) I know how to use varGroup as described in Pinheiro & Bates chapter 5, but in that library, I don't know how to force the random effects to be independent. (2) Using lmer (from lme4 library) I can force the random effects to be independent (using a description published by Bates in the R magazine in 2005) but I don't know how to allow the variance to depend on group. To be clear, the model I wan to fit is: Y_{ij} ~ beta_0 + beta_1*disease_{ij} + b_i 0 + b_i1*disease_{ij} + error_{ij} where b_i0 and b_i1 are independent Normal where error_{ij} = Normal(0, sd_case) if disease_{ij}= 1 error_{ij} = Normal(0, sd_control) if disease_{ij}= 2 i is an indicator of radiologist... a single radiologist does multiple cases and multiple controls. Thanks, Daryl ______________________________________________ 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.
