Dear All, I am trying to combine dependent p-values in R. May you please help me with this?
For independent pvalue combination, one of the popular way is fisher's method which I found the R code here (http://r.789695.n4.nabble.com/fisher-s-posthock-test-or-fisher-s-combination-test-td2195964.html#a2305025): fisher.comb <- function (pvalues) { df=length(pvalues) ch2=(-2*sum(log(pvalues))) return pchisq(ch2, df=df, lower.tail=FALSE) } For combination of dependent p-value, I could not find any R code here but there is a method from Brown (http://en.wikipedia.org/wiki/Extensions_of_Fisher%27s_method#Brown.27s_method), in which it is still combine p value in the same way, the only difference is the Chi square distirbution's variance is different from the previous method (using the covariance of the p values). I am not sure how to migrate from the traditional fisher's method to brown's method in R code. Because there is an introduction of covariance of the p values. And the normal R function for pchisq seems do not take parameters to change the variance of the chi square distribution. I am not sure if I fully grasped the statistics behind it to write correct function for combination of the dependent p-values. I hope someone having experience with this problem can help me. Thanks a lot in advance! Cheers, Shelly -- View this message in context: http://r.789695.n4.nabble.com/combined-dependent-pvalue-tp4647958.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
