How to check normality assumption in Mixed-effects models? I'm dealing with longitudinal data, and to take into account the dependence of observations within the cluster, I shall rely on a generalized linear mixed model. I have a continuous response variable, and I'd like to fit a Gaussian mixed model. However, plotting the density of the response (even after a log transformation) does not seem to be normal. It has two local maxima (where the second local is also a global maximum).
Is it appropriate to work with a Gaussian model?

If you have longitudinal data it might be a better idea to plot the response (y) as lines on the time axis (x). Then you can think about what model to use. You might prefer something different from a Gaussian mixed model, such as a GEE. What's the difference? Here
There are also other approaches that might be useful, but I don't have enough information on your problem to tell more.
