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.
Agreed. 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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
