On 26-Jun-07, at 8:12 AM, Mike Lawrence wrote: > Hi all, > Hopefully this will be quick, I'm looking for pointers to packages/ > functions that would allow me to calculate the power of a t.test when > the DV has measurement error. That is, I understand that, ceteris > paribus, experiments using measure with more error (lower > reliability) will have lower power.
I came across a reference (http://memetic.ca/reliability.pdf) that provides a formula for calculating the noncentrality parameter for tests using imperfect measures (see Eq. 4), as well as a table of some resulting power estimates. However, while I have created a (very slow) monte carlo function that so far as I can tell matches their results, when I attempt to implement their analytic solution it's way off. Can anyone see what I'm doing incorrectly? n=100 r=.5 #reliability e=.5 #effect size delta=(sqrt(r*n)/2)*e power.t.test(n,delta,sig.level=.05,alternative='one.sided') Two-sample t test power calculation n = 100 delta = 1.767767 sd = 1 sig.level = 0.05 power = 1 alternative = one.sided NOTE: n is number in *each* group Meanwhile, their tables and my monte carlo method say that the power in that circumstance should be .7 Help? Mike ______________________________________________ R-help@stat.math.ethz.ch 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.