[R] power analyses for mixed effects lmer models

Ben Bolker bolker at ufl.edu
Wed Jan 14 21:37:15 CET 2009

Greg Snow <Greg.Snow <at> imail.org> writes:

> My preferred method for this type of thing is to use simulation.  
> You have already done the hard parts in
> figuring out what your data is going to look like and how you plan
> to analyze it.  Now just write a function
> that will simulate data according to your pattern and with 
> the difference(s) that you want to compute the
> power for, then analyzes the simulated data and returns the
> value of interest (usually a single p-value,
> but could be something else).  Now run this function a bunch of times 
> (I would use the replicate function to
> do this) and see how often the conclusion of interest occurs 
> (p-val < alpha, or something else).  This is
> your estimate of power.


   There are power calculators out there for standard
ANOVA designs, even mixed models 
e.g. <http://www.stat.uiowa.edu/~rlenth/Power/> ,
but they're very unlikely to work for a crossed-random-effects
model with a continuous and a categorical predictor.

  If you have further questions along these lines I would
recommend the r-sig-mixed-models list.

  Ben Bolker

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