[R-sig-ME] lme vs. lmer and contrasts
robert.espesser at lpl-aix.fr
Wed Sep 30 10:09:50 CEST 2009
In the last discussion about "lme vs. lmer" (see below) , D. Bates
use anova between 2 mixed models to test a fixed effect.
1) Does it mean that mcmc pvalues have to be disregarded finally ?
2) When the fixed effect is a factor, anova returns the whole
significance of the factor,
but no information about the levels of the factor. How is it possible
to get it ?
(I am thinking about contrast analysis for a factor with more than 2
3) As a side question:
The mixed models are said to be robust to unbalanced data.
Let suppose a 4 levels factor, with a number of measures which is
each level (or at least for one level). In what extend can I trust
outputs of a mixed model with
typical contrasts (treatment, sdif, ordered) applied to such a factor ?
Thank you very much for your help
Laboratoire Parole et Langage,
Université de Provence/CNRS,
5 av. Pasteur, Aix en provence (France)
Douglas Bates a écrit :
> My general advice to those who are required to produce a p-value for a
> particular fixed-effects term in a mixed-effects model is to use a
> likelihood ratio test. Fit the model including that term using
> maximum likelihood (i.e. REML = FALSE), fit it again without the term
> and compare the results using anova.
> The likelihood ratio statistic will be compared to a chi-squared
> distribution to get a p-value and this process is somewhat suspect
> when the degrees of freedom would be small. However, so many other
> things could be going wrong when you are fitting complex models to few
> observations that this may be the least of your worries.
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