[R] Mixed Effects Model Power Calculations

[Shige Song]

Hi Dimitris,

Thank you so much! That really helps!

Shige

On 8/17/05, Dimitris Rizopoulos <dimitris.rizopoulos at med.kuleuven.be> wrote:
> I don't know what specific application Rick has in mind, but if there
> is possibility of missing values (which is common, e.g., in
> longitudinal studies) then this should also be taken into account in
> the power calculations.
> 
> Best,
> Dimitris
> 
> ----
> Dimitris Rizopoulos
> Ph.D. Student
> Biostatistical Centre
> School of Public Health
> Catholic University of Leuven
> 
> Address: Kapucijnenvoer 35, Leuven, Belgium
> Tel: +32/16/336899
> Fax: +32/16/337015
> Web: http://www.med.kuleuven.be/biostat/
>      http://www.student.kuleuven.be/~m0390867/dimitris.htm
> 
> 
> ----- Original Message -----
> From: "Henric Nilsson" <henric.nilsson at statisticon.se>
> To: <rab45+ at pitt.edu>
> Cc: <r-help at stat.math.ethz.ch>
> Sent: Wednesday, August 17, 2005 10:42 AM
> Subject: Re: [R] Mixed Effects Model Power Calculations
> 
> 
> >
> > On Ti, 2005-08-16, 21:17, rab45+ at pitt.edu skrev:
> >
> >> Is there an R package available that would facilitate doing a
> >> power/sample
> >> size analysis for linear mixed effects models?
> >
> > I'm not aware of such a package (others might be...).
> >
> > When it comes to sample size calculations, especially for tricky
> > designs
> > and/or advanced methodology, simulation is usually the best
> > approach. An
> > example using `lme' can be found at
> >
> > http://maven.smith.edu/~nhorton/R/
> >
> >
> > HTH,
> > Henric
> >
> >> I have seen the Java applets made available by Russell Length which
> >> would
> >> seem to be able to handle most any lme, but there is little
> >> documentation
> >> and it's not clear how the models need to be formulated.
> >>
> >> Rick B.
> >>
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