"estimateCommonDisp"(y, tol=1e-06, rowsum.filter=5, verbose=FALSE, ...) "estimateCommonDisp"(y, group=NULL, lib.size=NULL, tol=1e-06, rowsum.filter=5, verbose=FALSE, ...)
TRUEthen the estimated dispersion and BCV will be printed to standard output.
estimateCommonDisp.DGEListadds the following components to the input
estimateCommonDisp.defaultreturns a numeric scalar of the common dispersion estimate.
The CML method involves computing a matrix of quantile-quantile normalized counts, called pseudo-counts. The pseudo-counts are adjusted in such a way that the library sizes are equal for all samples, while preserving differences between groups and variability within each group. The pseudo-counts are included in the output of the function, but are intended mainly for internal edgeR use.