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

[Chris Howden]

They may not be addressing normality since we don’t always expect the
residuals to be normal e.g. if we are doing a GLMM with a Poisson or
binomial error distribution.

Chris Howden B.Sc. (Hons) GStat.
Founding Partner
Evidence Based Strategic Development, IP Commercialisation and Innovation,
Data Analysis, Modelling and Training
(mobile) 0410 689 945
(skype) chris.howden
chris at trickysolutions.com.au




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-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of
AvianResearchDivision
Sent: Wednesday, 21 May 2014 4:59 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Assessing Normality for Mixed Models

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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