I'm using a mixed model in
lme4) to analyze some repeated measures data. I have a response variable (fiber content of feces) and 3 fixed effects (body mass, etc.). My study only has 6 participants, with 16 repeated measures for each one (though two only have 12 repeats). The subjects are lizards that were given different combinations of food in different 'treatments'.
My question is: can I use subject ID as a random effect?
I know this is the usual course of action in longitudinal mixed effects models, to take account of the randomly sampled nature of the subjects and the fact that observations within subjects will be more closely correlated than those between subjects. But, treating subject ID as a random effect involves estimating a mean and variance for this variable.
Since I have only 6 subjects (6 levels of this factor), is this enough to get an accurate characterization of the mean and variance?
Does the fact that I have quite a few repeated measurements for each subject help in this regard (I don't see how it matters)?
Finally, If I can't use subject ID as a random effect, will including it as a fixed effect allow me to control for the fact that I have repeated measures?
Edit: I'd just like to clarify that when I say "can I" use subject ID as a random effect, I mean "is it a good idea to". I know I can fit the model with a factor with just 2 levels, but surely this would be in-defensible? I'm asking at what point does it become sensible to think about treating subjects as random effects? It seems like the literature advises that 5-6 levels is a lower bound. It seems to me that the estimates of the mean and variance of the random effect would not be very precise until there were 15+ factor levels.