> Date: Fri, 22 Jul 2005 08:14:53 -0500 (CDT) > From: jihoon at cs.wisc.edu > Subject: [BioC] p.value in eBayes( ) of limma > To: smyth at wehi.EDU.AU > Cc: bioconductor at stat.math.ethz.ch > > Dear Smyth, > > Hello, > > I'm curious about how p-value is calculated > (or which degree of freemdom is used) > from the R example of the function lm.series > in the package "limma" below. > > > > [R Example as in help page] > M <- matrix(rnorm(100*6,sd=0.3),100,6) > M[1,1:3] <- M[1,1:3] + 2 > # Design matrix includes two treatments, > # one for first 3 and one for last 3 arrays > design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1)) > fit <- lm.series(M,design=design) > eb <- ebayes(fit) > # Large values of eb$t indicate differential expression > qqt(eb$t[,1],df=fit$df+eb$df.prior) > abline(0,1) > > > > I thought following two codes should give same > results, but they didn't. > Which degree of freedom should I use to get > the same p.value? > > > eb$p.value[1,1] > 1-pt(eb$t[1,1], fit$df[1]+eb$df.prior) You are computing a one-sided p-value whereas limma computes two-sided p-values. Gordon > Thank you. > > > Jihoon Kim