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

[Farrar, David]

There could be, of course, normality or otherwise at multiple levels of variation.  
Also I would think the importance of normality (versus perhaps just not too heavy-tailed?) could depend on the inferences one wants to make.
David


-----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 Chris Howden
Sent: Tuesday, May 20, 2014 10:13 PM
To: AvianResearchDivision; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Assessing Normality for Mixed Models

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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