Hello, I am hoping someone could shed some light on power calculations for me. I have two small data sets of unequal sample size after NA removal (m = 5, f = 7). m <- c(2.0863, 2.1340, 2.1008, 1.9565, 2.0413, NA, NA) f <- c(1.8938, 1.9709, 1.8613, 2.0836, 1.9485, 2.0630, 1.9143) In a R help message/reply from Sep 30, 2001, it was noted that the "power.t.test" function assumes equal group sizes and that the groups have the same theoretical standard deviation. In "analyzing" this data, I ran a Welch Two Sample t-test and a Wilcoxon Rank Sum test on the data sets and both tests reveal a slight statistical difference for alpha = 0.05 (Welch Two Sample t-test p-value = 0.045 and Wilcoxon Rank Sum test p-value = 0.048). I suspect that the "power" of these tests will be quite low but I am trying to quantify it. Based on the insight in the R help message from 2001, I am not sure how to go about this with R. Is this feasible with R or is there another approach I should be considering altogether? Any insight would be most appreciated. Thanks a million....
It seems although your are trying to do a retrospective power calculation - not something to be encouraged. I don't think that power.t.test was designed to work on observed data in the way you seem to want to use it. You could use power.t.test to do a prospective calculation where you know the difference (delta) and the sd... Saghir -----Original Message----- From: r-help-bounces@stat.math.ethz.ch [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Jim BRINDLE Sent: Thursday, May 19, 2005 16:39 To: r-help@stat.math.ethz.ch Subject: [R] Power w/ unequal sample sizes Hello, I am hoping someone could shed some light on power calculations for me. I have two small data sets of unequal sample size after NA removal (m = 5, f 7). m <- c(2.0863, 2.1340, 2.1008, 1.9565, 2.0413, NA, NA) f <- c(1.8938, 1.9709, 1.8613, 2.0836, 1.9485, 2.0630, 1.9143) In a R help message/reply from Sep 30, 2001, it was noted that the "power.t.test" function assumes equal group sizes and that the groups have the same theoretical standard deviation. In "analyzing" this data, I ran a Welch Two Sample t-test and a Wilcoxon Rank Sum test on the data sets and both tests reveal a slight statistical difference for alpha = 0.05 (Welch Two Sample t-test p-value = 0.045 and Wilcoxon Rank Sum test p-value = 0.048). I suspect that the "power" of these tests will be quite low but I am trying to quantify it. Based on the insight in the R help message from 2001, I am not sure how to go about this with R. Is this feasible with R or is there another approach I should be considering altogether? Any insight would be most appreciated. Thanks a million.... ______________________________________________ R-help@stat.math.ethz.ch mailing list stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! R-project.org/posting-guide.html --------------------------------------------------------- Legal Notice: This electronic mail and its attachments are i...{{dropped}}