[R] unequal variance assumption for lme (mixed effect model)

[shirley zhang]

Hi Simon,

Thanks for your reply. Your reply reminds me that book. I've read it
long time ago, but haven't  try the weights option in my projects
yet:)

Is the heteroscedastic test always less powerful because we have to
estimate the within group variance from the given data?

Should we check whether each group has equal variance before using
weights=varIdent()? If we should, what is the function for linear
mixed model?

Thanks,
Shirley

On 6/27/07, Simon Blomberg <s.blomberg1 at uq.edu.au> wrote:
> The default settings for lme do assume equal variances within groups.
> You can change that by using the various varClasses. see ?varClasses. A
> simple example would be to allow unequal variances across groups. So if
> your call to lme was:
>
> lme(...,random=~1|group,...)
>
> then to allow each group to have its own variance, use:
>
> lme(...,random=~1|group, weights=varIdent(form=~1|group),...)
>
> You really really should read Pinheiro & Bates (2000). It's all there.
>
> HTH,
>
> Simon.
>
> , On Wed, 2007-06-27 at 21:55 -0400, shirley zhang wrote:
> > Dear Douglas and R-help,
> >
> > Does lme assume normal distribution AND equal variance among groups
> > like anova() does? If it does, is there any method like unequal
> > variance T-test (Welch T) in lme when each group has unequal variance
> > in my data?
> >
> > Thanks,
> > Shirley
> >
> > ______________________________________________
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> --
> Simon Blomberg, BSc (Hons), PhD, MAppStat.
> Lecturer and Consultant Statistician
> Faculty of Biological and Chemical Sciences
> The University of Queensland
> St. Lucia Queensland 4072
> Australia
>
> Room 320, Goddard Building (8)
> T: +61 7 3365 2506
> email: S.Blomberg1_at_uq.edu.au
>
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>
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