Essentially, I have two collinear variables which could be seen as either random or as fixed effects, a dependent variable I'm fitting the model to, and a variable that's assuredly a random effect.
Dependent var: Number of neuron spikes (
FiringRate) in a specific region of mousebrain
Time at which data sample was taken (on a linear scale in days -- so day two would be 2, day 5 would be 5, and so on)
Age of the mouse in days (so there's definitely collinearity between this and the
Time variable, but there are enough mice of different ages to make this worthwhile as a separate variable)
Subject -- "Name" (ID number) of the mouse
Essentially, I'm wondering if it would be appropriate to run two LMEs. In the first, I'd treat
Subject as random variables in order to control for the effects of
Age (and thus the collinearity between
Time) and see if Time is a significant predictor of the # of spikes (dependent variable). In the second, I'd enter
Subject as random variables to see if
Age was a significant predictor.
library(lme4) a = lmer(FiringRate ~ Time + (1|Age) + (1|Subject)) b = lmer(FiringRate ~ Age + (1|Time) + (1|Subject))