Model comparison using BIC, AIC, -2Log

Hi Luis

I largely agree with Mike's answer and have the following additional
comments: The decision of whether a variable is taken as fixed or
random often rests on subject specific matter. An important question
is: Can the levels of the variable be considered as coming from a
normal distribution? But other aspects also play a role, such as the
number of realized levels of the variable (with only few levels, it
will often be appropriate to treat the variable as fixed anyhow). The
models rests on different distributional assumptions, so the decision
is often based on weighing the appropriateness of these assumptions.

To give more specific advise on the actual model comparison (ignoring
the question of the appropriateness of the comparison), it matters
whether you are thinking in terms of linear mixed models or
generalized linear mixed models. In the former case assuming you have
only one random effect and assuming lme is sufficient, you can do

fm.lme <- lme(....)
fm.lm <- lm(...)
anova(fm.lme, fm.lm)

If you are thinking in terms of generalized linear mixed models, and
you are using lmer, then maybe you can use something like

deviance(fm.lmer <- lmer(...))
deviance(fm.glm <- glm(...))

however, the reference distribution for the difference in deviance
depends on the actual body of the function calls.

Regards
Rune

2008/7/4 Luis Orlindo Tedeschi <luis.tedeschi at gmail.com>:

Model comparison using BIC, AIC, -2Log

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