Hi all,

Thank you for the answers so far.  I have been looking at
qqnorm(residuals(model)) and notice that there isn't a linear line, but
rather some skewing at both ends.  When I try various transformations (log,
sqrt, boxcox, etc...), I don't see much of an improvement in this plot.
I'm just wondering if these models are robust to such deviations or not.  I
was also looking for some methods in various R packages that quantitatively
assess these assumptions.

If it helps with answers, I am running the following models over 59 males
with roughly 15 observations per male for a total of 872 observations:

model1<-lmer(Response~Predictor+(1|Male)) or
model2<-lmer(Response~Predictor+(Predictor|Male)) or
model3<-lmer(Response~Predictor+(1|Male)+(Predictor+0|Male))

where my response and predictor values are continuous.  I have run models
where I have within individual mean centered my predictor, along with using
the mean value of the predictor for each male for each observation of each
male to separate the within and between subject effects as per van de Pol
and Wright (2009), so my model looks like

model4<-lmer(Response~Predictor(within)+Predictor(between)+(1|Male))

The Predictor(between) is never significant, so I end up running

model5<-lmer(Response~Predictor+Predictor(between)+(1|Male)

to verify that there are no between subject effects.

Since I never see any between subject effects, I end up running 'model1'
from above, where the predictor variable is the actual unadjusted observed
values, which now represents only the within subject effect as tested in
'model4' and 'model5'.

Once I have my fixed effects structure set, I have set the random effects
structure using LRT fitted by ML to test the significant of random slopes
and correlation between slopes and intercepts.

I now am at the point where I just need to make sure I am meeting model
assumptions, which is the subject of my initial email.  I hope this extra
detail sheds some light on my particular issue and helps with providing
advice.

Jacob


On Wed, May 21, 2014 at 10:00 AM, Farrar, David <Farrar.David@epa.gov>wrote:

>
> 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@r-project.org [mailto:
> r-sig-mixed-models-bounces@r-project.org] On Behalf Of Chris Howden
> Sent: Tuesday, May 20, 2014 10:13 PM
> To: AvianResearchDivision; r-sig-mixed-models@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@trickysolutions.com.au
>
>
>
>
> Disclaimer: The information in this email and any attachments to it are
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>
> -----Original Message-----
> From: r-sig-mixed-models-bounces@r-project.org
> [mailto:r-sig-mixed-models-bounces@r-project.org] On Behalf Of
> AvianResearchDivision
> Sent: Wednesday, 21 May 2014 4:59 AM
> To: r-sig-mixed-models@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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