Dear Will, residuals() should take both the fixed and random effects into account. Can you give us a reproducible example if you get something different?
Use residuals(model, type = "normalized") if you also want to account for the correlation structure. What do you want to do with the residuals? Model them? In that case I would suggest that you model the response variable directly. Note that the parameter estimates of the random effects and the correlation structure can (will) change if you add variables to the model. Best regards, Thierry PS Use the mixed models list for this kind of questions about mixed models. ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & 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 will.ea...@gmx.net > Verzonden: donderdag 29 juli 2010 16:07 > Aan: r-help@r-project.org > Onderwerp: [R] Residuals of mixed effects model > > Dear all, > > how do I get the residuals from a lme() output objects which > are adjusted for fixed AND (!) random effects? > > I tried residuals(), but it seems they just give me the > residuals adjusted for the fixed effects of the regression model. > > The model I use is: > lme.out <- lme(data=MyDataInLongFormat,fixed= outcome~1, > random= ~ 1|individual, correlation=corSymm(form = ~time|individual)) > > Actually, I use only the intercept in the fixed part of the > predictor, and I want to get residuals which are adjusted for > the fixed part (intercept) and the random effect, ie to get > rid of the correlatedness of individual measures across time. > This way I want to get data where I can treat the measures > per time point as independent groups. Makes sense? > > Thanks in advance, > > Will > > ______________________________________________ > 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.
