I am currently working with expression data in LIMMA and have been asked to fit a robust linear model:
lm.fit.rob <- lmFit(object=y$E,design=m.matrix, method="robust") lm.fit.rob.bayes <- eBayes(lm.fit.rob) lm.fit.rob.bayes.tt <- topTable(lm.fit.rob.bayes,coef="Group")
It is easy to find that lmFit uses mrlm, and that mrlm uses rlm. However, rlm does not generate p-values, and from what I have read (e.g. http://r.789695.n4.nabble.com/p-values-td803236.html ) it is not a trivial thing to do. What confuses me is that topTable generates p-values, and I can't find in the documentation how and on which assumptions.
The robust models do greatly improve my p-values, but can they be trusted?