On 26-Jun-07, at 2:36 PM, Mike Lawrence wrote: > 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
Found it; I was using power.t.test without being thorough in reading its details. Sorry for the spam, and for anyone that's interested, here's the final analytic solution: #get power for a t.test, incorporating measurement error. #n = total number of participants across your 2 groups #r = estimated reliability of the measure used #e = measured effect size get.power=function(n,r,e,tails=2,alpha=.05){ d=(sqrt(r*n)/2)*e a=1-ifelse(tails==2,alpha/2,alpha) p=1-pt(qt(a,n-2),n-2,d) return(p) } ______________________________________________ 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.