Hi Nils,

I would be inclined to do it by eye. Plot your *model* residuals for each
measurement. You can generally get away with the smallest being about 1/3
the size of the largest before you run into issues. Lattice plots can be
useful for this.

As for normality - qq plots are your friend...the automated tests can be
overly sensitive - normality is not as important as variance for instance.

But you have to remember that its the normality and variance of residuals
thats important, not the raw data.

I've written a very basic function to do some of this...

source("http://db.tt/VtvcjJFL") # to get it

arguments:
modcheck(model, dataframe, res.type, other, wait)

model is the fitted model object (e.g. lm, mer ...[doesnt work on merMod
yet])
dataframe - the dataframe used to make the model
res.type - residual type (normalised or whatever), defaults to pearson
other - if you want to plot against parameters not in the model, but are in
dataframe
wait - logical to say whether to wait before changing plots, defaults to
true

HTH

Alan


--------------------------------------------------
Email: aghaynes@gmail.com
Mobile: +41794385586
Skype: aghaynes


On 18 October 2012 17:20, Nils Gülzow <Nils.Guelzow@gmx.de> wrote:

> Dear community,
>
> I have general question related to how I can test for normality and
> homogeneity of variance (e.g. leveneTest) if my dependent variable
> (df) is measured over several time points.
>
> Normally, I use the shapiro.wilks-test and the leveneTest to ensure
> that I can use the ANOVA. Is that also possible for repeated
> measurements  to check if your data are normally distributed and
> homogenized to continue with a rm-ANOVA?
> Or is that not necessary?
>
> Thanks in advance,
> Nils
> Nils Gülzow
>
> Müllerstr. 21
> 26384 Wilhelmshaven
> Tel.: 04421/3597682
> E-mail: nils.guelzow@gmx.de
>
>
>
>
>
>
>
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