Assessing Normality for Mixed Models

Hi Jacob,

You should do similar normality check as you would do for linear models, 
I usually use the qqplot, you can use qqnorm(resid(model)) and 
qqline(resid(model)). Then another assumptions from linear mixed models 
is that the random effect are normally distributed with a mean of 0, you 
can use qqnorm(unlist(model)) and qqline(unlist(model)) if you have only 
one random term.

So two things should be normally distributed in linear mixed models: the 
residuals and the random effects.

When you have a low number of level in the random effects normality will 
in some case not be reached just due to the small number of levels, I am 
not aware of ways to account for this, I would either include the random 
effect as fixed effect or use simulation.

Sincerely yours,
Lionel

Hi All,

After doing some extensive googling, searching for ways to assess normality
for linear mixed models, I can honestly say my head is swimming in
different proposed techniques that may or may not be valid.  Also, when
reading the literature, I find that few studies that use linear mixed
models and random regression actually explicitly address how they assess
normality.  What are the rules with normality with mixed models (if there
are any) and what are your techniques to assess normality?  Any input that
you can provide would be great and hopefully we help to settle my mind on
this issue.

Thank you,
Jacob

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