lme4 and deviance
I share your concern about how to define the scaled deviance. The trick, as you say, is deciding what the saturated mixed model is. I don't know of a good way of defining it. It might be best to just refer to the log-likelihood and not derive anything called "deviance".
On Thu, May 21, 2015 at 9:09 AM Nad?ge Jacot <Nadege.Jacot at unige.ch> wrote:
Dear list,
I'm trying to understand why the deviance function returns -2 log
likelihood in lme4 and not the "true" deviance as with lm().
The scaled deviance is defined as the difference of -2 log likelihood
between the model of interest and the saturated model but what is the
saturated mixed model? For balanced data, some authors (e.g. Hoffman 2014,
Longitudinal Analysis: Modeling Within-Person Fluctuation and Change)
define the saturated mixed model as a model with saturated means (one fixed
effect for each time point) and unstructured variance but such a model is
not available for unbalanced data.
And if we can compute the scaled deviance, what is the deviance?
Thanks in advance for any hint.
Nad?ge Jacot
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