alanc at umit.maine.edu
Sat Mar 29 20:52:58 CET 2008
Doug Bates writes on r-sig-mixed-models at r-project.org on Saturday, March 29, 2008 at 7:00 AM -0500 wrote about his planned book on multilevel modelling in R:
>I emphasize graphical displays of the data and aspects
>of the fitted models and inferences based on MCMC samples from the
>posterior distribution of the model parameters.
(n)lme handled correlated error terms, but lme4 does not. Leaving aside the superior algorithms in lme4, this appears to be the major impediment to considering lme4 capabilities as a superset of (n)lme capabilities.
But what do I do if I've got, for example, autocorrelated error terms? Is there a way to "trick" lme4 into handling that (perhaps something analogous to the "random effect variance per treatment group in lmer" thread that David Afshartous and I
participated in)? Is there instead a good argument for ignoring it? It seems like something that would arise in practice in a non-negligible amount of problems in real data. Will the upcoming book give some advice on how to address this?
I can produce self-contained reproducible code if necessary, but I don't think it is.
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