[R-sig-ME] Assessing Normality for Mixed Models

Lionel hughes.dupond at gmx.de
Tue May 20 21:25:31 CEST 2014

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,

On 20/05/2014 20:59, AvianResearchDivision wrote:
> 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
> 	[[alternative HTML version deleted]]
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