complex residual variance
Not in lmer(). But you can do it with lme() from the nlme package. Thierry ------------------------------------------------------------------------ ---- 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.Onkelinx at 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-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Sven De Maeyer Verzonden: maandag 31 mei 2010 15:47 Aan: r-sig-mixed-models at r-project.org Onderwerp: [R-sig-ME] complex residual variance Hi all, I'm not a frequent user of Lme4 and I have the habit to use mlwin for multilevel analyses. Nevertheless I'm willing to master lme4 further. In my research I'm often faced with the situation where level 1 variance (or residual variance in the mixed effects framework) isn't constant. E.g. I have longitudinal data on children, with multiple measurements (observations) of these children at a certain moment. As children get older these measurements seem to vary more and more. So the residual variance itself is a function of time. As a consequence there is heteroscedasticity. Is there a possibility to explicitly model this in lme4? I know how to do it in mlwin, but is it possible in lmer? With kind regards Sven De Maeyer
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