I am trying to model y, a continuous variable that only takes positive values with fixed and random effects.
This is my first approach, using
log(y) ~ x1 + x2 + x3 + (1|plot) + (1|id)
The fit is not perfect but the residuals look roughly OK I guess.
In my second approach I am using
y ~ x1 + x2 + x3 + (1|plot) + (1|id)
family = Gamma(link = "inverse")
The fit gets a bit better. But I am unsure if using the much more complex
glmer is justified or if I should stick to
lmer? On what grounds should I decide with which model to go?