On Feb 13, 2008 2:37 PM, Matthias Gondan <[EMAIL PROTECTED]> wrote: > Hi Eleni, > > The problem of this approach is easily explained: Under the Null > hypothesis, the P values > of a significance test are random variables, uniformly distributed in > the interval [0, 1]. It > is easily seen that the lowest of these P values is not any 'better' > than the highest of the > P values. > > Best wishes, > > Matthias >
Correct me if I'm wrong, but isn't that the point? I assume that the hypothesis is that one or more of these genes are true predictors, i.e. for these genes the p-value should be significant. For all the other genes, the p-value is uniformly distributed. Using a significance level of 0.01, and an a priori knowledge that there are significant genes, you will end up with on the order of 20 genes, some of which are the "true" predictors, and the rest being false positives. this set of 20 genes can then be further analysed. A much smaller and easier problem to solve, no? /Gustaf -- Gustaf Rydevik, M.Sci. tel: +46(0)703 051 451 address:Essingetorget 40,112 66 Stockholm, SE skype:gustaf_rydevik ______________________________________________ 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.
