Dear R-list, I am sorry for my shortage of stat knowlege. I want know how to conduct a hypothesis test : Ho:|E(X)|=|E(Y)|<->H1:otherwise.
Actually, in my study, X and Y is two observations of bias, where bias=u^hat-u, u is a parameter I concerned. Given X=(u^hat_xi - u) and Y=(u^hat_yi - u), I want to know which bias is smaller, or the absolute expection of which is smaller. Due to limit of sample size, I think we cannot make a conclusion by comparing the absolute mean value of each sample. So I turn to a means of hypothesis testing as I post in the title. Now my strategy is using permutation test. Like this: permutation.test.bias=function(x,y,mc=1000){ n1=length(x) n2=length(y) n=n1+n2 xy=c(x,y) dbar=abs(abs(mean(x))-abs(mean(y))) z=c() for(i in 1:mc){ mn=sample(1:n,n1) z[i]=abs(mean(xy[mn]))-abs(mean(xy[-mn])) } p.value=sum(abs(z)>dbar)/mc p.value } Although it seems plausible, this function doesn't work > n=1000 > x=rnorm(n)+10 > y=rnorm(n)-10 > permutation.test.bias(x,y) [1] 0 I think there should be other test methods for this problem. Thanks for any suggestion/solution. -- Junjie Li, [EMAIL PROTECTED] Undergranduate in DEP of Tsinghua University, [[alternative HTML version deleted]] ______________________________________________ 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.